Bibliography Bibliographie

Every source the site draws on, in one list: the works cited at the bottom of content pages and on glossary entries. 577 references. Toutes les sources du site en une seule liste : les ouvrages cités au bas des pages de contenu et dans les entrées du glossaire. 577 références.

Adelani, D. I., Agostinelli, V., Anastasopoulos, A., Bentivogli, L., Bojar, O., et al. (2026). Speech translation and metrics in 2026: Findings of the IWSLT campaign. Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), 336–422. https://aclanthology.org/2026.iwslt-1.39/
Agarwal, D., Naaman, M., & Vashistha, A. (2025). AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–21. https://doi.org/10.1145/3706598.3713564
Agarwal, R., Singh, A., Zhang, L., Bohnet, B., Rosias, L., Chan, S., et al. (2024). Many-Shot In-Context Learning. Advances in Neural Information Processing Systems 37 (NeurIPS 2024). https://proceedings.neurips.cc/paper_files/paper/2024/hash/8cb564df771e9eacbfe9d72bd46a24a9-Abstract-Conference.html
Akpan, N. (2016). Google artificial intelligence beats world champion in first game of Go tournament. PBS NewsHour, 9 March 2016. https://www.pbs.org/newshour/science/google-artificial-intelligence-beats-world-champion-in-first-game-of-go-tournament
Alexandrov, A., Raychev, V., Dimitrov, D. I., Zhang, C., Vechev, M., & Toutanova, K. (2024). BgGPT 1.0: Extending English-centric LLMs to other languages. arXiv preprint 2412.10893. https://arxiv.org/abs/2412.10893
Ali, M., Fromm, M., Thellmann, K., Rutmann, R., Lübbering, M., et al. (2024). Tokenizer Choice For LLM Training: Negligible or Crucial?. Findings of the Association for Computational Linguistics: NAACL 2024, 3907–3924. https://aclanthology.org/2024.findings-naacl.247/
Allauzen, A., & Bonneau-Maynard, H. (2008). Training and Evaluation of POS Taggers on the French MULTITAG Corpus. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, 3373–3377. https://aclanthology.org/L08-1368/
American National Standards Institute (1990). American National Standard for Information Systems - Dictionary for Information Systems (ANSI X3.172-1990). American National Standards Institute. https://nvlpubs.nist.gov/nistpubs/Legacy/FIPS/fipspub11-3.pdf
American Psychological Association (2018). Metacognition. APA Dictionary of Psychology. https://dictionary.apa.org/metacognition
An, J., Huang, D., Lin, C., & Tai, M. (2025). Measuring gender and racial biases in large language models: Intersectional evidence from automated resume evaluation. PNAS Nexus, 4(3), pgaf089. https://doi.org/10.1093/pnasnexus/pgaf089
Anderson, B. R., Shah, J. H., & Kreminski, M. (2024). Homogenization Effects of Large Language Models on Human Creative Ideation. Proceedings of the 16th Conference on Creativity & Cognition, 413–425. https://doi.org/10.1145/3635636.3656204
Andrä, S. C., & Schütz, J. (2011). The semantically-enriched translation interoperability protocol. Proceedings of the Workshop on Language Resources, Technology and Services in the Sharing Paradigm (IJCNLP 2011), 25–30. https://aclanthology.org/W11-3304.pdf
Anthony, L. (2026). AntConc (Version 4.4.2) [Computer software]. Waseda University, Tokyo. https://www.laurenceanthony.net/software/antconc/
Anthropic (2026). Claude's Constitution. Anthropic, 22 January 2026, CC0 1.0. https://www.anthropic.com/constitution
(2023). Arendse & 42 others v Meta Platforms, Inc & 3 others. Employment and Labour Relations Court at Nairobi, Constitutional Petition E052 of 2023. https://new.kenyalaw.org/akn/ke/judgment/keelrc/2023/1398/eng@2 June 2023
Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4), 555–596. https://doi.org/10.1162/coli.07-034-R2
Askell, A., Bai, Y., Chen, A., Drain, D., Ganguli, D., Henighan, T., et al. (2021). A General Language Assistant as a Laboratory for Alignment. arXiv:2112.00861 (preprint). https://arxiv.org/abs/2112.00861
Aycock, S., Stap, D., Wu, D., Monz, C., & Sima'an, K. (2025). Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?. The Thirteenth International Conference on Learning Representations (ICLR 2025). https://proceedings.iclr.cc/paper_files/paper/2025/hash/20f44da80080d76bbc35bca0027f14e6-Abstract-Conference.html
Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. Third International Conference on Learning Representations (ICLR 2015), oral presentation; arXiv:1409.0473. https://arxiv.org/abs/1409.0473
Bai, X., Wang, A., Sucholutsky, I., & Griffiths, T. L. (2025). Explicitly unbiased large language models still form biased associations. Proceedings of the National Academy of Sciences, 122(8), e2416228122. https://doi.org/10.1073/pnas.2416228122
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., et al. (2022b). Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv:2204.05862 (preprint). https://arxiv.org/abs/2204.05862
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., et al. (2022a). Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073 (preprint). https://arxiv.org/abs/2212.08073
Balloccu, S., Schmidtová, P., Lango, M., & Dušek, O. (2024). Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 67–93. https://doi.org/10.18653/v1/2024.eacl-long.5
Banerjee, S., Chakravarthi, B. R., & McCrae, J. P. (2024). Large Language Models for Few-Shot Automatic Term Extraction. In Natural Language Processing and Information Systems (NLDB 2024), Lecture Notes in Computer Science (pp. 137–150). Springer. https://doi.org/10.1007/978-3-031-70239-6_10
Bañón, M., Chen, P., Haddow, B., Heafield, K., Hoang, H., Esplà-Gomis, M., Forcada, M. L., Kamran, A., Kirefu, F., Koehn, P., Ortiz Rojas, S., Pla Sempere, L., Ramírez-Sánchez, G., Sarrías, E., Strelec, M., Thompson, B., Waites, W., Wiggins, D., & Zaragoza, J. (2020). ParaCrawl: Web-Scale Acquisition of Parallel Corpora. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4555–4567. https://doi.org/10.18653/v1/2020.acl-main.417
(2025). Bartz v. Anthropic PBC. United States District Court for the Northern District of California, 4:24-cv-05417. https://www.courtlistener.com/docket/69058235/bartz-v-anthropic-pbc/
Basil, S., Shapiro, I., Shapiro, D., Mollick, E., Mollick, L., & Meincke, L. (2025). Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy. arXiv:2512.05858 (technical report). https://arxiv.org/abs/2512.05858
Basile, V., Fell, M., Fornaciari, T., Hovy, D., Paun, S., Plank, B., et al. (2021). We Need to Consider Disagreement in Evaluation. Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future. https://doi.org/10.18653/v1/2021.bppf-1.3
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122
Baumgarten, S., & Tieber, M. (Eds.) (2025). The Routledge Handbook of Translation Technology and Society. Routledge. https://doi.org/10.4324/9781003271314
Becker, J., Rush, N., Barnes, E., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv preprint arXiv:2507.09089. http://arxiv.org/abs/2507.09089
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8). https://doi.org/10.1109/TPAMI.2013.50
Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., et al. (2024). Managing extreme AI risks amid rapid progress. Science, 384(6698), 842–845. https://doi.org/10.1126/science.adn0117
Benkler, Y., Faris, R., & Roberts, H. (2018). Epistemic Crisis. Network Propaganda: Manipulation, Disinformation, and Radicalization in American Politics (Oxford University Press). https://doi.org/10.1093/oso/9780190923624.003.0001
Bergmanis, T., & Goldwater, S. (2018). Context Sensitive Neural Lemmatization with Lematus. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 1391–1400. https://aclanthology.org/N18-1126/
Bertsch, A., Ivgi, M., Xiao, E., Alon, U., Berant, J., Gormley, M. R., & Neubig, G. (2025). In-Context Learning with Long-Context Models: An In-Depth Exploration. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025), 12119–12149. https://doi.org/10.18653/v1/2025.naacl-long.605
Bestvater, S., Smith, A., TerBush, C., Baronavski, C., & Chavda, J. (2026). How Much of the Internet Is Written With AI?. Pew Research Center, Data Labs. https://www.pewresearch.org/data-labs/2026/08/20/how-much-of-the-internet-is-written-with-ai/
Biber, D. (2011). Corpus linguistics and the study of literature: Back to the future?. Scientific Study of Literature, 1(1), 15–23. https://doi.org/10.1075/ssol.1.1.02bib
Biswas, J., Schoepp, S., Vasan, G., Opipari, A., Zhang, A., Hu, Z., Joseph, S., Lease, M., Li, J. J., Stone, P., Wagstaff, K. L., Taylor, M. E., & Jenkins, O. C. (2026). AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot. arXiv preprint arXiv:2604.13940. http://arxiv.org/abs/2604.13940
Boguslav, M., & Cohen, K. B. (2017). Inter-Annotator Agreement and the Upper Limit on Machine Performance: Evidence from Biomedical Natural Language Processing. Studies in Health Technology and Informatics, 245, 298–302. https://doi.org/10.3233/978-1-61499-830-3-298
Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C., et al. (2015). Findings of the 2015 Workshop on Statistical Machine Translation. Proceedings of the Tenth Workshop on Statistical Machine Translation, 1–46. https://aclanthology.org/W15-3001/
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Advances in Neural Information Processing Systems 29 (NIPS 2016). https://proceedings.neurips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html
Bostrom, N. (2003). Ethical Issues in Advanced Artificial Intelligence. In I. Smit et al. (Eds.), Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in Artificial Intelligence (Vol. 2, pp. 12–17). International Institute of Advanced Studies in Systems Research and Cybernetics. https://nickbostrom.com/ethics/ai
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. https://openlibrary.org/isbn/9780199678112
Bove, A., Di Cataldo, P., & Maestroni, D. (2026). Evaluating the Effect of Prompt Language on LLM-based Translation: Evidence from Spanish<>Italian Translation. Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), 800–812. https://aclanthology.org/2026.eamt-1.51/
Bowker, L. (2002). Computer-Aided Translation Technology: A Practical Introduction. University of Ottawa Press. https://doi.org/10.1353/book6554
Bowker, L. (2023). Translating for Canada, eh? (version 2). University of Ottawa, via eCampusOntario Pressbooks; also available in French as « Vous traduisez pour le Canada ? ». https://ecampusontario.pressbooks.pub/translatingforcanadav2/
Bowker, L., & Fisher, D. (2010). Computer-aided translation. Handbook of Translation Studies (John Benjamins Publishing Company). https://doi.org/10.1075/hts.1.comp2
Bowker, L., & Pearson, J. (2002). Working with Specialized Language: A Practical Guide to Using Corpora. Routledge. https://doi.org/10.4324/9780203469255
Brezina, V. (2018). Statistics in Corpus Linguistics: A Practical Guide. Cambridge University Press. https://doi.org/10.1017/9781316410899
Briakou, E., Luo, J., Cherry, C., & Freitag, M. (2024). Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts. Proceedings of the Ninth Conference on Machine Translation (WMT 2024), 1301–1317. https://doi.org/10.18653/v1/2024.wmt-1.123
Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., et al. (2023). Machine culture. Nature Human Behaviour, 7(11), 1855–1868. https://doi.org/10.1038/s41562-023-01742-2
Brooks, C., Eggert, S., & Peskoff, D. (2024). The Rise of AI-Generated Content in Wikipedia. Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia, 67–79. https://doi.org/10.18653/v1/2024.wikinlp-1.12
Brown, M. A., Gruen, A., Maldoff, G., Messing, S., Sanderson, Z., & Zimmer, M. (2025). Web scraping for research: Legal, ethical, institutional, and scientific considerations. Big Data & Society. https://doi.org/10.1177/20539517251381686
Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer, R. L. (1993). The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics, 19(2), 263–311. https://aclanthology.org/J93-2003/
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
Brynjolfsson, E., Chandar, B., & Chen, R. (2026). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab, working paper (revised 12 August 2026). https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Brysbaert, M., Stevens, M., Mandera, P., & Keuleers, E. (2016). How Many Words Do We Know? Practical Estimates of Vocabulary Size Dependent on Word Definition, the Degree of Language Input and the Participant's Age. Frontiers in Psychology, 7, 1116. https://doi.org/10.3389/fpsyg.2016.01116
Budzyń, K., Romańczyk, M., Kitala, D., Kołodziej, P., Bugajski, M., Adami, H. O., et al. (2025). Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. The Lancet Gastroenterology & Hepatology, 10(10), 896–903. https://doi.org/10.1016/S2468-1253(25)00133-5
Bulté, B., & Rigouts Terryn, A. (2026). LLMs and Cultural Values: The Impact of Prompt Language and Explicit Cultural Framing. Computational Linguistics, 52(2), 407–494. https://doi.org/10.1162/COLI.a.583
Burnard, L. (2004). Metadata for Corpus Work. In M. Wynne (Ed.), Developing Linguistic Corpora: a Guide to Good Practice. AHDS Literature, Languages and Linguistics. https://users.ox.ac.uk/~martinw/dlc/chapter3.htm
Burtch, G., Lee, D., & Chen, Z. (2024). The consequences of generative AI for online knowledge communities. Scientific Reports, 14, 10413. https://doi.org/10.1038/s41598-024-61221-0
Cabezas-Clavijo, Á., Magadán-Díaz, M., Rivas-García, J. I., & Sidorenko-Bautista, P. (2024). This Book is Written by ChatGPT: A Quantitative Analysis of ChatGPT Authorships Through Amazon.com. Publishing Research Quarterly, 40(2), 147–163. https://doi.org/10.1007/s12109-024-09998-w
Cai, Z., Wang, Y., Sun, Q., Wang, R., Gu, C., Yin, W., et al. (2025). Holistic Evaluation of Multimodal LLMs on Spatial Intelligence. arXiv preprint arXiv:2508.13142. http://arxiv.org/abs/2508.13142
Caliskan, A., Ajay, P. P., Charlesworth, T., Wolfe, R., & Banaji, M. R. (2022). Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 156–170. https://doi.org/10.1145/3514094.3534162
Canadian Intellectual Property Office (2021). Copyright infringement. Innovation, Science and Economic Development Canada. https://ised-isde.canada.ca/site/canadian-intellectual-property-office/en/copyright-infringement
Cao, Y., Zhou, L., Lee, S., Cabello, L., Chen, M., & Hershcovich, D. (2023). Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study. Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP), 53–67. https://doi.org/10.18653/v1/2023.c3nlp-1.7
Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., Oprea, A., & Raffel, C. (2021). Extracting Training Data from Large Language Models. Proceedings of the 30th USENIX Security Symposium (USENIX Security 21), 2633–2650. https://www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting
Chakrabarty, T., Ginsburg, J. C., & Dhillon, P. (2025). Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers. arXiv preprint arXiv:2510.13939. https://doi.org/10.48550/arXiv.2510.13939
Champagne, G. (2004). The Economic Value of Terminology: an Exploratory Study. Report submitted to the Translation Bureau of Canada, 30 April 2004. Guy Champagne Inc., Montreal.
Chandra, B., Dunietz, J., Roberts, K., Lee, Y., Fontana, P., & Awad, G. (2024). Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency. NIST Trustworthy and Responsible AI, NIST AI 100-4, National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-4
Chapekis, A., & Lieb, A. (2025). Google users are less likely to click on links when an AI summary appears in the results. Pew Research Center, short read. https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/
Chatterji, A., Cunningham, T., Deming, D. J., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025). How People Use ChatGPT. NBER Working Paper 34255, National Bureau of Economic Research. https://doi.org/10.3386/w34255
Chen, B., & Cherry, C. (2014). A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU. Proceedings of the Ninth Workshop on Statistical Machine Translation, 362–367. https://doi.org/10.3115/v1/W14-3346
Chen, J., Lin, H., Han, X., & Sun, L. (2024). Benchmarking Large Language Models in Retrieval-Augmented Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17754–17762. https://doi.org/10.1609/aaai.v38i16.29728
Chen, N., Ghassemi, M., Koniusz, P., Pascanu, R., & Lin, H.-T. (2025a). NeurIPS 2025 Policy on the Use of Large Language Models. Conference on Neural Information Processing Systems (NeurIPS). https://neurips.cc/Conferences/2025/LLM
Chen, Y., Benton, J., Radhakrishnan, A., Uesato, J., Denison, C., Schulman, J., et al. (2025b). Reasoning Models Don't Always Say What They Think. arXiv preprint arXiv:2505.05410. https://doi.org/10.48550/arXiv.2505.05410
Cheng, J., Marone, M., Weller, O., Lawrie, D., Khashabi, D., & Van Durme, B. (2024). Dated Data: Tracing Knowledge Cutoffs in Large Language Models. Proceedings of the First Conference on Language Modeling (COLM 2024). https://openreview.net/forum?id=wS7PxDjy6m
Chesney, R., & Citron, D. K. (2019). Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. California Law Review, 107(6), 1753–1820. https://scholarship.law.bu.edu/faculty_scholarship/640
Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., et al. (2024). Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. Proceedings of the 41st International Conference on Machine Learning (PMLR 235). https://proceedings.mlr.press/v235/chiang24b.html
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., et al. (2023). PaLM: Scaling Language Modeling with Pathways. Journal of Machine Learning Research, 24(240), 1–113. https://jmlr.org/papers/v24/22-1144.html
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep Reinforcement Learning from Human Preferences. Advances in Neural Information Processing Systems 30 (NIPS 2017). https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html
Chun, Y., Kim, M., Kim, D., Park, C., & Lim, H. (2025). Enhancing automatic term extraction with large language models via syntactic retrieval. Findings of the Association for Computational Linguistics: ACL 2025, 9916–9926. https://aclanthology.org/2025.findings-acl.516/
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021). All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text. Proceedings of ACL-IJCNLP 2021 (Long Papers), 7282–7296. https://doi.org/10.18653/v1/2021.acl-long.565
Coalition for Content Provenance and Authenticity (n.d.). C2PA Specifications. Coalition for Content Provenance and Authenticity, version 2.4. https://spec.c2pa.org/specifications/specifications/2.4/index.html
Committee on Publication Ethics (COPE) (2023). Authorship and AI tools. COPE position statement. https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools
Common Crawl (n.d.). About. Common Crawl. https://commoncrawl.org/about
Competition and Markets Authority (2024). CMA outlines growing concerns in markets for AI Foundation Models. GOV.UK, press release, 11 April 2024. https://www.gov.uk/government/news/cma-outlines-growing-concerns-in-markets-for-ai-foundation-models
Competition Bureau Canada (2024). Artificial intelligence and competition: discussion paper - March 2024. Government of Canada / Competition Bureau Canada. https://publications.gc.ca/site/eng/9.935280/publication.html
Cooke, D., Edwards, A., Barkoff, S., & Kelly, K. (2025). As Good as a Coin Toss: Human Detection of AI-Generated Content. Communications of the ACM, 68(10), 100–109. https://doi.org/10.1145/3729417
Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: Why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 50(7), 1087–1097. https://doi.org/10.1080/02602938.2025.2503964
Council of Europe, Steering Committee for Media and Information Society (CDMSI) (2021). Content moderation: Best practices towards effective legal and procedural frameworks for self-regulatory and co-regulatory mechanisms of content moderation. Guidance note. Council of Europe. https://rm.coe.int/content-moderation-en/1680a2cc18
Dahan, N., Peng, Z., Yvon, F., & Bawden, R. (2026). Improving Term Evaluation in Machine Translation: Variation Matters. Proceedings of the 17th Conference of the Association for Machine Translation in the Americas (Research Track). https://aclanthology.org/2026.amta-research.7/
Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., et al. (2024). Scalable watermarking for identifying large language model outputs. Nature, 634(8035), 818–823. https://doi.org/10.1038/s41586-024-08025-4
DCMI (Dublin Core Metadata Initiative) (n.d.). Metadata Basics. https://www.dublincore.org/resources/metadata-basics/
de Marneffe, M.-C., Manning, C. D., Nivre, J., & Zeman, D. (2021). Universal Dependencies. Computational Linguistics, 47(2), 255–308. https://doi.org/10.1162/coli_a_00402
de Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. https://doi.org/10.1016/j.joule.2023.09.004
de Wynter, A. (2026). Is In-Context Learning Learning?. The Fourteenth International Conference on Learning Representations (ICLR 2026). https://arxiv.org/abs/2509.10414
del Rio-Chanona, R. M., Laurentsyeva, N., & Wachs, J. (2024). Large language models reduce public knowledge sharing on online Q&A platforms. PNAS Nexus, 3(9), pgae400. https://doi.org/10.1093/pnasnexus/pgae400
Delavande, J., Pierrard, R., & Luccioni, A. S. (2025). Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Models. arXiv preprint arXiv:2509.19222. https://doi.org/10.48550/arXiv.2509.19222
Dell'Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., et al. (2026). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838
Demirci, O., Hannane, J., & Zhu, X. (2025). Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms. Management Science, 71(10), 8097–8108. https://doi.org/10.1287/mnsc.2024.05420
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). https://doi.org/10.18653/v1/N19-1423
DiResta, R., & Goldstein, J. A. (2024). How spammers and scammers leverage AI-generated images on Facebook for audience growth. Harvard Kennedy School (HKS) Misinformation Review. https://doi.org/10.37016/mr-2020-151
Diwan, A., Srinivasan, A., Harwath, D., & Choi, E. (2024). Textless speech-to-speech translation with limited parallel data. Findings of the Association for Computational Linguistics: EMNLP 2024, 16302–16311. https://aclanthology.org/2024.findings-emnlp.951/
Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290
Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018). The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1383–1392. https://doi.org/10.18653/v1/P18-1128
Drouin, P. (2003). Term extraction using non-technical corpora as a point of leverage. Terminology, 9(1), 99–115. https://doi.org/10.1075/term.9.1.06dro
Drouin, P., Morel, J.-B., & L'Homme, M.-C. (2020). Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features. In B. Daille, K. Kageura & A. Rigouts Terryn (Eds.), Proceedings of the 6th International Workshop on Computational Terminology (CompuTerm 2020) (pp. 1–7). European Language Resources Association. https://aclanthology.org/2020.computerm-1.1/
Dugan, L., Hwang, A., Trhlík, F., Zhu, A., Ludan, J. M., Xu, H., Ippolito, D., & Callison-Burch, C. (2024). RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 12463–12492. https://doi.org/10.18653/v1/2024.acl-long.674
Dumas, C., Wendler, C., Veselovsky, V., Monea, G., & West, R. (2025). Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformers. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 31822–31841. https://doi.org/10.18653/v1/2025.acl-long.1536
Edman, L., Schmid, H., & Fraser, A. (2024). CUTE: Measuring LLMs' Understanding of Their Tokens. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 3017–3026. https://aclanthology.org/2024.emnlp-main.177/
ELIS Research (2025). European Language Industry Survey 2025. Trends, expectations and concerns of the European language industry. ELIA, EMT, EUATC, FIT Europe, GALA, LIND and Women in Localization. https://knowledge-centre-translation-interpretation.ec.europa.eu/sites/default/files/ckeditor5-files/ELIS-2025_Report.pdf
ELIS Research (2026). European Language Industry Survey 2026: Trends, expectations and concerns of the European language industry. ELIA, EMT, EUATC, FIT Europe, GALA, LIND and Women in Localization. https://elis-survey.org/wp-content/uploads/2026/03/ELIS-2026-Report.pdf
Eloundou, T., Beutel, A., Robinson, D. G., Gu, K., Brakman, A.-L., Mishkin, P., Shah, M., Heidecke, J., Weng, L., & Kalai, A. T. (2025). First-Person Fairness in Chatbots. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). https://openreview.net/forum?id=TlAdgeoDTo
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308. https://doi.org/10.1126/science.adj0998
Elsworth, C., Huang, K., Patterson, D., Schneider, I., Sedivy, R., Goodman, S., Townsend, B., Ranganathan, P., Dean, J., Vahdat, A., Gomes, B., & Manyika, J. (2025). Measuring the environmental impact of delivering AI at Google Scale. arXiv preprint arXiv:2508.15734. https://doi.org/10.48550/arXiv.2508.15734
Epoch AI (2026). Data on AI models. Online database, CC BY, epoch.ai (retrieved 9 September 2026). https://epoch.ai/data/ai-models
Etxaniz, J., Azkune, G., Soroa, A., Lopez de Lacalle, O., & Artetxe, M. (2024). Do Multilingual Language Models Think Better in English?. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), 550–564. https://doi.org/10.18653/v1/2024.naacl-short.46
European Union (2019). Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC. Official Journal of the European Union, L 130, 92–125. http://data.europa.eu/eli/dir/2019/790/oj
European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). Official Journal of the European Union, L series. http://data.europa.eu/eli/reg/2024/1689/oj
Fairwork (2025). Fairwork Cloudwork Ratings 2025. Advancing standards in digital labour and AI supply chain governance. Oxford Internet Institute, University of Oxford. https://fairwork.oii.ox.ac.uk/wp-content/uploads/sites/11/2025/05/Fairwork-Cloudwork-Report-2025-FINAL.pdf
Fantinuoli, C. (2016). InterpretBank: Redefining computer-assisted interpreting tools. Proceedings of the 38th Conference Translating and the Computer, London, 42–52. https://aclanthology.org/2016.tc-1.5/
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
Fernandes, P., Deutsch, D., Finkelstein, M., Riley, P., Martins, A. F. T., Neubig, G., et al. (2023). The Devil Is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation. Proceedings of the Eighth Conference on Machine Translation (WMT 2023), 1066–1083. https://doi.org/10.18653/v1/2023.wmt-1.100
Ferstler, Y., Podoxin, A., Brassington, T., Laperrière, G., Grundkiewicz, R., Meurs, M.-J., et al. (2026). AI translation of literary texts is "fine", but readers still prefer human translations. arXiv preprint arXiv:2606.26040; accepted at Findings of EMNLP 2026. https://doi.org/10.48550/arXiv.2606.26040
Firth, J. R. (1957). A synopsis of linguistic theory 1930–1955. Studies in Linguistic Analysis (Special volume of the Philological Society), Blackwell, 1–32. https://quoteinvestigator.com/2022/09/18/word-company/
Fitterer, S., Gangl, D., & Ulbrich, J. (2025). Testing English News Articles for Lexical Homogenization Due to Widespread Use of Large Language Models. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), 1239–1245. https://doi.org/10.18653/v1/2025.acl-srw.95
Fleisig, E., Smith, G., Bossi, M., Rustagi, I., Yin, X., & Klein, D. (2024). Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 13541–13564. https://aclanthology.org/2024.emnlp-main.750/
Francis, W. N., & Kučera, H. (1979). Manual of information to accompany a standard corpus of present-day edited American English, for use with digital computers. Department of Linguistics, Brown University, Providence, RI (revised and amplified edition; first published 1964). https://listings.lib.msu.edu/public-corpora/cd421/manuals/brown/INDEX.HTM
Frank, J., Herbert, F., Ricker, J., Schönherr, L., Eisenhofer, T., Fischer, A., Dürmuth, M., & Holz, T. (2024). A Representative Study on Human Detection of Artificially Generated Media Across Countries. 2024 IEEE Symposium on Security and Privacy (SP), 55–73. https://doi.org/10.1109/SP54263.2024.00159
Frantzi, K., Ananiadou, S., & Mima, H. (2000). Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal on Digital Libraries, 3(2), 115–130. https://doi.org/10.1007/s007999900023
Freitag, M., Foster, G., Grangier, D., Ratnakar, V., Tan, Q., & Macherey, W. (2021). Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation. Transactions of the Association for Computational Linguistics, 9, 1460–1474. https://doi.org/10.1162/tacl_a_00437
Gabriel, I. (2020). Artificial Intelligence, Values, and Alignment. Minds and Machines. https://doi.org/10.1007/s11023-020-09539-2
GALA (Globalization and Localization Association) (2005). TMX 1.4b Specification. https://resources.gala-global.org/tbx14b/
Galibert, O., Camelin, N., Deléglise, P., & Rosset, S. (2016). Estimation de la qualité d'un système de reconnaissance de la parole pour une tâche de compréhension. Actes de la conférence conjointe JEP-TALN-RECITAL 2016, vol. 1, 274–282. https://aclanthology.org/2016.jeptalnrecital-jep.31/
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024). Bias and Fairness in Large Language Models: A Survey. Computational Linguistics, 50(3), 1097–1179. https://doi.org/10.1162/coli_a_00524
Ganguli, D., Lovitt, L., Kernion, J., Askell, A., Bai, Y., Kadavath, S., et al. (2022). Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. arXiv:2209.07858 (preprint). https://arxiv.org/abs/2209.07858
Garante per la protezione dei dati personali (2023). Artificial intelligence: stop to ChatGPT by the Italian SA. Press release, 31 March 2023. https://www.garanteprivacy.it:443/home/docweb/-/docweb-display/docweb/9870847
Garante per la protezione dei dati personali (2024). ChatGPT, the Italian Data Protection Authority closes the preliminary investigation. Press release, 20 December 2024. https://www.garanteprivacy.it:443/home/docweb/-/docweb-display/docweb/10085432
Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024). Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 7765–7784. https://aclanthology.org/2024.emnlp-main.444/
(2025). GEMA v. OpenAI. Landgericht München I (Munich Regional Court I), 42 O 14139/24, judgment of 11 November 2025. https://www.justiz.bayern.de/gerichte-und-behoerden/landgericht/muenchen-1/presse/2025/11.php
Geng, S., Cooper, H., Moskal, M., Jenkins, S., Berman, J., Ranchin, N., et al. (2025). JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models. arXiv preprint arXiv:2501.10868. https://doi.org/10.48550/arXiv.2501.10868
Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6. https://www.mdpi.com/2075-4698/15/1/6
Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., et al. (2024). Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data. ICML 2024 Workshop on Foundation Models in the Wild (arXiv:2404.01413). https://arxiv.org/abs/2404.01413
(2025). Getty Images (US) Inc & Ors v Stability AI Limited. High Court of Justice, Business and Property Courts of England and Wales, Intellectual Property List (Chd), [2025] EWHC 2863 (Ch). https://caselaw.nationalarchives.gov.uk/ewhc/ch/2025/2863
(2025). Getty Images (US), Inc & Ors v Stability AI Ltd (Re Form of Order). High Court of Justice, Business and Property Courts of England and Wales, Intellectual Property List (Chd), [2025] EWHC 3343 (Ch). https://caselaw.nationalarchives.gov.uk/ewhc/ch/2025/3343
Goldman, O., Caciularu, A., Eyal, M., Cao, K., Szpektor, I., & Tsarfaty, R. (2024). Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance. Findings of the Association for Computational Linguistics: ACL 2024, 2274–2286. https://aclanthology.org/2024.findings-acl.134/
Gonen, H., & Goldberg, Y. (2019). Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 609–614. https://doi.org/10.18653/v1/N19-1061
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
Google (n.d.). Function calling with the Gemini API. Google AI for Developers. https://ai.google.dev/gemini-api/docs/function-calling
Google (n.d.). Refine Google searches. Google Search Help. https://support.google.com/websearch/answer/2466433
Gornostay, T. (2010). Terminology Management in Real Use. Proceedings of the 5th International Biannual Conference “Applied Linguistics in Research and Education” in Memoriam Rajmund Piotrowski (1922-2009), Saint Petersburg.
Gourabathina, A., Gerych, W., Pan, E., & Ghassemi, M. (2025). The Medium is the Message: How Non-Clinical Information Shapes Clinical Decisions in LLMs. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT '25), 1805–1828. https://doi.org/10.1145/3715275.3732121
Government of Canada (1985). Copyright Act (R.S.C., 1985, c. C-42). Justice Laws Website, Department of Justice Canada. https://laws-lois.justice.gc.ca/eng/acts/C-42/
Government of Canada (2023). Government of Canada launches consultation on the implications of generative artificial intelligence for copyright. Innovation, Science and Economic Development Canada, news release, 12 October 2023. https://www.canada.ca/en/innovation-science-economic-development/news/2023/10/government-of-canada-launches-consultation-on-the-implications-of-generative-artificial-intelligence-for-copyright.html
Goyal, S., Baek, C., Kolter, J. Z., & Raghunathan, A. (2025). Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance. The Thirteenth International Conference on Learning Representations (ICLR 2025). https://arxiv.org/abs/2410.10796
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., et al. (2024). The Llama 3 Herd of Models. arXiv:2407.21783 (preprint). https://arxiv.org/abs/2407.21783
Gray, M. L., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Houghton Mifflin Harcourt. https://search.worldcat.org/title/1052904468
Griot, M., Hemptinne, C., Vanderdonckt, J., & Yuksel, D. (2025). Large Language Models lack essential metacognition for reliable medical reasoning. Nature Communications, 16. https://doi.org/10.1038/s41467-024-55628-6
Grobelnik, M., Perset, K., & Russell, S. (2024). What is AI? Can you make a clear distinction between AI and non-AI systems?. OECD.AI Policy Observatory. https://oecd.ai/en/wonk/definition
Guerreiro, N. M., Rei, R., van Stigt, D., Coheur, L., Colombo, P., & Martins, A. F. T. (2024). xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection. Transactions of the Association for Computational Linguistics, 12, 979–995. https://doi.org/10.1162/tacl_a_00683
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., et al. (2025). DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature, 645(8081), 633–638. https://doi.org/10.1038/s41586-025-09422-z
Guo, J., Hao, Z., Wang, C., Fan, C., Luo, T., Li, H., et al. (2026). From Question Answering to Task Completion: A Survey on Agent System and Harness Design. arXiv preprint arXiv:2606.20683. https://arxiv.org/abs/2606.20683
Hadra, M., Cambridge, K., & Mesbah, M. (2026). Evaluating the accuracy and reliability of AI content detectors in academic contexts. International Journal for Educational Integrity, 22(1), 4. https://doi.org/10.1007/s40979-026-00213-1
Han, T., Wang, Z., Fang, C., Zhao, S., Ma, S., & Chen, Z. (2025). Token-Budget-Aware LLM Reasoning. Findings of the Association for Computational Linguistics: ACL 2025. https://doi.org/10.18653/v1/2025.findings-acl.1274
Han, Y., Wu, Z., Li, P., Wierman, A., & Ren, S. (2026). Health-Informed Computing: Estimating and Addressing the Public Health Impact of Data Centers. arXiv preprint arXiv:2412.06288, version 4. https://arxiv.org/abs/2412.06288
Hanna, M., & Bojar, O. (2021). A Fine-Grained Analysis of BERTScore. Proceedings of the Sixth Conference on Machine Translation, 507–517. https://aclanthology.org/2021.wmt-1.59/
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018). Federated Learning for Mobile Keyboard Prediction. arXiv:1811.03604. https://arxiv.org/abs/1811.03604
Hassan, H., Aue, A., Chen, C., Chowdhary, V., Clark, J., Federmann, C., et al. (2018). Achieving Human Parity on Automatic Chinese to English News Translation. arXiv preprint arXiv:1803.05567. https://doi.org/10.48550/arXiv.1803.05567
He, J., Rungta, M., Koleczek, D., Sekhon, A., Wang, F. X., & Hasan, S. (2024). Does Prompt Formatting Have Any Impact on LLM Performance?. Preprint, arXiv:2411.10541. https://arxiv.org/abs/2411.10541
Heaven, W. D. (2026). Moltbook was peak AI theater. MIT Technology Review. https://www.technologyreview.com/2026/02/06/1132448/moltbook-was-peak-ai-theater/
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2021). Measuring Massive Multitask Language Understanding. Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021). https://openreview.net/forum?id=d7KBjmI3GmQ
HeyGen (2026). Privacy Policy. https://www.heygen.com/privacy
Hicks, M. T., Humphries, J., & Slater, J. (2024). ChatGPT is bullshit. Ethics and Information Technology, 26, 38. https://doi.org/10.1007/s10676-024-09775-5
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., et al. (2022). An empirical analysis of compute-optimal large language model training. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://proceedings.neurips.cc/paper_files/paper/2022/hash/c1e2faff6f588870935f114ebe04a3e5-Abstract-Conference.html
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2020). The Curious Case of Neural Text Degeneration. International Conference on Learning Representations (ICLR 2020). https://openreview.net/forum?id=rygGQyrFvH
Howcroft, D. M., Belz, A., Clinciu, M.-A., Gkatzia, D., Hasan, S. A., Mahamood, S., et al. (2020). Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions. Proceedings of the 13th International Conference on Natural Language Generation, 169–182. https://doi.org/10.18653/v1/2020.inlg-1.23
Hripcsak, G., & Rothschild, A. S. (2005). Agreement, the F-measure, and reliability in information retrieval. Journal of the American Medical Informatics Association, 12(3), 296–298. https://doi.org/10.1197/jamia.M1733
Hu, H., Vamvas, J., & Sennrich, R. (2025). Source-primed multi-turn conversation helps large language models translate documents. Findings of the Association for Computational Linguistics: EMNLP 2025, 23702–23712. https://aclanthology.org/2025.findings-emnlp.1289/
Hui, X., Reshef, O., & Zhou, L. (2024). The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market. Organization Science, 35(6), 1977–1989. https://doi.org/10.1287/orsc.2023.18441
Humlum, A., & Vestergaard, E. (2025a). Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI. NBER Working Paper 33777 (revised 2026). https://doi.org/10.3386/w33777
Humlum, A., & Vestergaard, E. (2025b). The unequal adoption of ChatGPT exacerbates existing inequalities among workers. Proceedings of the National Academy of Sciences, 122(1), e2414972121. https://doi.org/10.1073/pnas.2414972121
Hutchins, J. (2003). ALPAC: The (In)Famous Report. In S. Nirenburg, H. L. Somers & Y. A. Wilks (Eds.), Readings in Machine Translation (pp. 131–136). The MIT Press. https://doi.org/10.7551/mitpress/5779.003.0014
Hutchins, W. J. (2004). The Georgetown-IBM experiment demonstrated in January 1954. Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers, 102–114. https://aclanthology.org/2004.amta-papers.12/
Ibrahim, H., Liu, F., Asim, R., Battu, B., Benabderrahmane, S., Alhafni, B., et al. (2023). Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Scientific Reports, 13(1), 12187. https://doi.org/10.1038/s41598-023-38964-3
ICLR (2026). ICLR 2026 Reviewer Guide. International Conference on Learning Representations. https://iclr.cc/Conferences/2026/ReviewerGuide
ICLR 2026 Program Chairs (2026). A Retrospective on the ICLR 2026 Review Process. ICLR Blog. https://blog.iclr.cc/2026/03/31/a-retrospective-on-the-iclr-2026-review-process/
ICML 2026 Program Chairs (2026). Reviewer Instructions. International Conference on Machine Learning (ICML 2026). https://icml.cc/Conferences/2026/ReviewerInstructions
Indurthi, S. R., Zhou, W., Chollampatt, S., Agrawal, R., Song, K., Zhao, L., & Zhu, C. (2024). Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets. Findings of the Association for Computational Linguistics: EMNLP 2024, 2306–2323. https://doi.org/10.18653/v1/2024.findings-emnlp.128
International Energy Agency (2025). Energy and AI. International Energy Agency, Paris. https://www.iea.org/reports/energy-and-ai
International Energy Agency (2026). Key Questions on Energy and AI. International Energy Agency, Paris. https://www.iea.org/reports/key-questions-on-energy-and-ai
International Organization for Standardization (2015). Translation services — Requirements for translation services. International Organization for Standardization. https://www.iso.org/standard/59149.html
International Organization for Standardization (2024). ISO 5060:2024: Translation services — Evaluation of translation output — General guidance. International Organization for Standardization, Geneva. https://www.iso.org/standard/80701.html
International Organization for Standardization (n.d.). Carbon footprint: Measuring and reducing our environmental impact. ISO. https://www.iso.org/renewable-energy/carbon-footprint
International Organization for Standardization & International Electrotechnical Commission (2017). Information technology — Cloud computing — Interoperability and portability (ISO/IEC 19941:2017). ISO/IEC. https://www.iso.org/standard/66639.html
International Organization for Standardization & International Electrotechnical Commission (2021). Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making. ISO/IEC TR 24027:2021, International Organization for Standardization. https://www.iso.org/standard/77607.html
International Organization for Standardization & International Electrotechnical Commission (2022). Information technology — Artificial intelligence — Artificial intelligence concepts and terminology (ISO/IEC 22989:2022). ISO/IEC. https://www.iso.org/standard/74296.html
International Telecommunication Union (2022). Environmental efficiency and impacts on United Nations Sustainable Development Goals of data centres and cloud computing. ITU-T L.Sup55 (10/2022), International Telecommunication Union. https://www.itu.int/rec/T-REC-L.Sup55
International Telecommunication Union (2025). Measuring What Matters: How to Assess AI's Environmental Impact. ITU publication S-GEN-GDA.001-2025. https://www.itu.int/hub/publication/s-gen-gda-001-2025/
Iranzo-Sánchez, J., Iranzo-Sánchez, J., Giménez, A., Civera, J., & Juan, A. (2024). Segmentation-free streaming machine translation. Transactions of the Association for Computational Linguistics, 12, 1104–1121. https://aclanthology.org/2024.tacl-1.61/
ISO (2019). ISO 1087:2019 Terminology work and terminology science — Vocabulary. International Organization for Standardization, 2nd ed.. https://www.iso.org/standard/62330.html
ISO (2019). ISO 30042:2019. Management of terminology resources — TermBase eXchange (TBX). International Organization for Standardization. https://www.iso.org/standard/62510.html
ISO (2024). ISO 21720:2024. XLIFF (XML Localization Interchange File Format) — Version 2.1. International Organization for Standardization. https://www.iso.org/standard/87344.html
Jackson, S., & Gallagher, B. (2026). System Transition. In SEBoK Editorial Board, The Guide to the Systems Engineering Body of Knowledge (SEBoK), v. 2.14, N. Hutchison (Ed.). Hoboken, NJ: The Trustees of the Stevens Institute of Technology. https://sebokwiki.org/wiki/System_Transition
Jakesch, M., Hancock, J. T., & Naaman, M. (2023). Human heuristics for AI-generated language are flawed. Proceedings of the National Academy of Sciences, 120(11), e2208839120. https://doi.org/10.1073/pnas.2208839120
Jakubíček, M., Kilgarriff, A., Kovář, V., Rychlý, P., & Suchomel, V. (2013). The TenTen Corpus Family. 7th International Corpus Linguistics Conference (CL 2013), Lancaster, 125–127. https://www.sketchengine.eu/wp-content/uploads/The_TenTen_Corpus_2013.pdf
Jaźwińska, K., & Chandrasekar, A. (2025). AI Search Has a Citation Problem. Tow Center for Digital Journalism, Columbia Journalism Review, 6 March 2025. https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php
Jensen, B. (2024). Exploring the Complex Ethical Challenges of Data Annotation. Stanford Institute for Human-Centered Artificial Intelligence. https://hai.stanford.edu/news/exploring-complex-ethical-challenges-data-annotation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., et al. (2023). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12). https://doi.org/10.1145/3571730
Jiang, F., Xu, Z., Niu, L., Xiang, Z., Ramasubramanian, B., Li, B., & Poovendran, R. (2024). ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 15157–15173. https://aclanthology.org/2024.acl-long.809/
Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The State and Fate of Linguistic Diversity and Inclusion in the NLP World. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://aclanthology.org/2020.acl-main.560/
Jung, K.-H. (2023). Uncover This Tech Term: Foundation Model. Korean Journal of Radiology, 24(10). https://doi.org/10.3348/kjr.2023.0790
Jurafsky, D., & Martin, J. H. (2024). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. Online draft of 20 August 2024, 3rd edition. https://web.stanford.edu/~jurafsky/slp3/ed3bookaug20_2024.pdf
Jurafsky, D., & Martin, J. H. (2026). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. Online draft, 3rd edition. https://web.stanford.edu/~jurafsky/slp3/
Justeson, J. S., & Katz, S. M. (1995). Technical terminology: some linguistic properties and an algorithm for identification in text. Natural Language Engineering, 1(1), 9–27. https://doi.org/10.1017/S1351324900000048
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., et al. (2022). Language Models (Mostly) Know What They Know. arXiv preprint arXiv:2207.05221. https://doi.org/10.48550/arXiv.2207.05221
(2025). Kadrey v. Meta Platforms, Inc.. United States District Court for the Northern District of California, 3:23-cv-03417. https://www.courtlistener.com/docket/67569326/kadrey-v-meta-platforms-inc/
Kageura, K., & Umino, B. (1996). Methods of automatic term recognition: A review. Terminology, 3(2), 259–289. https://doi.org/10.1075/term.3.2.03kag
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. https://us.macmillan.com/books/9780374533557/thinkingfastandslow/
Kamali, N., Nakamura, K., Kumar, A., Chatzimparmpas, A., Hullman, J., & Groh, M. (2025). Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–26. https://doi.org/10.1145/3706598.3713962
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., et al. (2020). Scaling Laws for Neural Language Models. arXiv:2001.08361 (preprint). https://arxiv.org/abs/2001.08361
Karpathy, A. (2023). [1hr Talk] Intro to Large Language Models. YouTube (Andrej Karpathy channel). https://www.youtube.com/watch?v=zjkBMFhNj_g
Karpinska, M., & Iyyer, M. (2023). Large language models effectively leverage document-level context for literary translation, but critical errors persist. Proceedings of the Eighth Conference on Machine Translation (WMT 2023). https://aclanthology.org/2023.wmt-1.41/
Kendro, K., Maloney, J., & Jarvis, S. (2026). Do Large Language Models Produce Texts With “Human-Like” Lexical Diversity? Evidence From Four ChatGPT Models. International Journal of Applied Linguistics, 36(3), 2822–2834. https://doi.org/10.1111/ijal.70115
Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15(1), 17458. https://doi.org/10.1038/s41598-025-97652-6
Kilgarriff, A., & Grefenstette, G. (2003). Introduction to the Special Issue on the Web as Corpus. Computational Linguistics, 29(3). https://doi.org/10.1162/089120103322711569
Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Rychlý, P., & Suchomel, V. (2014). The Sketch Engine: ten years on. Lexicography, 1(1), 7–36. https://doi.org/10.1007/s40607-014-0009-9
Kimi Team (2026). Kimi K3: Open Frontier Intelligence. arXiv:2607.24653 (preprint). https://arxiv.org/abs/2607.24653
Kirk, R., Mediratta, I., Nalmpantis, C., Luketina, J., Hambro, E., Grefenstette, E., & Raileanu, R. (2024). Understanding the Effects of RLHF on LLM Generalisation and Diversity. The Twelfth International Conference on Learning Representations (ICLR 2024). https://openreview.net/forum?id=PXD3FAVHJT
Knight, W. (2016). Google's AI Masters the Game of Go a Decade Earlier Than Expected. MIT Technology Review, 27 January 2016. https://www.technologyreview.com/2016/01/27/109178/googles-ai-masters-the-game-of-go-a-decade-earlier-than-expected/
Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225. https://doi.org/10.1016/j.caeai.2024.100225
Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances, 11(27), eadt3813. https://doi.org/10.1126/sciadv.adt3813
Kocmi, T., & Federmann, C. (2023a). GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4. Proceedings of the Eighth Conference on Machine Translation (WMT 2023), 768–775. https://doi.org/10.18653/v1/2023.wmt-1.64
Kocmi, T., & Federmann, C. (2023b). Large Language Models Are State-of-the-Art Evaluators of Translation Quality. Proceedings of the 24th Annual Conference of the European Association for Machine Translation, 193–203. https://aclanthology.org/2023.eamt-1.19/
Kocmi, T., Artemova, E., Avramidis, E., Bawden, R., Bojar, O., Dranch, K., et al. (2025). Findings of the WMT25 General Machine Translation Shared Task: Time to Stop Evaluating on Easy Test Sets. Proceedings of the Tenth Conference on Machine Translation (WMT 2025), Volume 2: Shared Task Papers, 355–413. https://doi.org/10.18653/v1/2025.wmt-1.22
Kocmi, T., Avramidis, E., Bawden, R., Bojar, O., Dvorkovich, A., Federmann, C., et al. (2023). Findings of the 2023 Conference on Machine Translation (WMT23): LLMs Are Here but Not Quite There Yet. Proceedings of the Eighth Conference on Machine Translation (WMT 2023), Singapore, 1–42. https://doi.org/10.18653/v1/2023.wmt-1.1
Kocmi, T., Avramidis, E., Bawden, R., Bojar, O., Dvorkovich, A., Federmann, C., et al. (2024b). Findings of the WMT24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet. Proceedings of the Ninth Conference on Machine Translation (WMT 2024), 1–46. https://doi.org/10.18653/v1/2024.wmt-1.1
Kocmi, T., Federmann, C., Grundkiewicz, R., Junczys-Dowmunt, M., Matsushita, H., & Menezes, A. (2021). To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation. Proceedings of the Sixth Conference on Machine Translation (WMT 2021), 478–494. https://aclanthology.org/2021.wmt-1.57/
Kocmi, T., Zouhar, V., Avramidis, E., Grundkiewicz, R., Karpinska, M., Popović, M., et al. (2024a). Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation. Proceedings of the Ninth Conference on Machine Translation (WMT 2024), 1440–1453. https://doi.org/10.18653/v1/2024.wmt-1.131
Koehn, P. (2005). Europarl: A Parallel Corpus for Statistical Machine Translation. Proceedings of Machine Translation Summit X, Phuket, 79–86. https://aclanthology.org/2005.mtsummit-papers.11/
Koehn, P. (2010). Statistical Machine Translation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815829
Koehn, P., Och, F. J., & Marcu, D. (2003). Statistical Phrase-Based Translation. Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, 127–133. https://aclanthology.org/N03-1017/
Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., & Goel, S. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, 117(14), 7684–7689. https://doi.org/10.1073/pnas.1915768117
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://proceedings.neurips.cc/paper_files/paper/2022/hash/8bb0d291acd4acf06ef112099c16f326-Abstract-Conference.html
Kommers, C., Duede, E., Gordon, J., Holtzman, A., McNulty, T., Stewart, S., et al. (2026). Why Slop Matters. ACM AI Letters. https://doi.org/10.1145/3786777
Kondratyuk, D., Gavenčiak, T., Straka, M., & Hajič, J. (2018). LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4921–4928. https://aclanthology.org/D18-1532/
Kong, A., Zhao, S., Chen, H., Li, Q., Qin, Y., Sun, R., Zhou, X., Wang, E., & Dong, X. (2024). Better Zero-Shot Reasoning with Role-Play Prompting. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 4099–4113. https://aclanthology.org/2024.naacl-long.228/
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv:2506.08872 (preprint). https://arxiv.org/abs/2506.08872
Koster, M., Illyes, G., Zeller, H., & Sassman, L. (2022). Robots Exclusion Protocol. RFC 9309, Internet Engineering Task Force. https://doi.org/10.17487/RFC9309
Kreps, S., McCain, R. M., & Brundage, M. (2022). All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation. Journal of Experimental Political Science, 9(1), 104–117. https://doi.org/10.1017/XPS.2020.37
Laban, P., Hayashi, H., Zhou, Y., & Neville, J. (2026). LLMs Get Lost In Multi-Turn Conversation. The Fourteenth International Conference on Learning Representations (ICLR 2026). https://openreview.net/forum?id=VKGTGGcwl6
Lai, H., & Li, W. (2026). The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish–Chinese Journalistic Translation. Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), 883–901. https://aclanthology.org/2026.eamt-1.57/
Lambert, N. (2026). Reinforcement Learning from Human Feedback. arXiv:2504.12501, v11 (3 August 2026); continually updated book, online at rlhfbook.com. https://arxiv.org/abs/2504.12501
Lambert, N., Morrison, J., Pyatkin, V., Huang, S., Ivison, H., Brahman, F., et al. (2025). Tulu 3: Pushing Frontiers in Open Language Model Post-Training. Conference on Language Modeling (COLM 2025). https://openreview.net/forum?id=i1uGbfHHpH
Läubli, S., Castilho, S., Neubig, G., Sennrich, R., Shen, Q., & Toral, A. (2020). A Set of Recommendations for Assessing Human–Machine Parity in Language Translation. Journal of Artificial Intelligence Research, 67. https://doi.org/10.1613/jair.1.11371
Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? A case for document-level evaluation. Proceedings of EMNLP 2018, 4791–4796. https://aclanthology.org/D18-1512/
Lavie, A., Hanneman, G., Agrawal, S., Kanojia, D., Lo, C., Zouhar, V., et al. (2025). Findings of the WMT25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help. Proceedings of the Tenth Conference on Machine Translation (WMT 2025), Volume 2: Shared Task Papers, 436–483. https://doi.org/10.18653/v1/2025.wmt-1.24
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25), article 1121, 1–22. https://doi.org/10.1145/3706598.3713778
Lee, M. H. J., Montgomery, J. M., & Lai, C. K. (2024). Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). https://doi.org/10.1145/3630106.3658975
Lefever, E., & Rigouts Terryn, A. (2024). Computational Terminology. In Y. Peng et al. (Eds.), New Advances in Translation Technology: Applications and Pedagogy (pp. 141–159). Springer Nature. https://doi.org/10.1007/978-981-97-2958-6_8
Lefever, E., Macken, L., & Hoste, V. (2009). Language-independent bilingual terminology extraction from a multilingual parallel corpus. Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009), 496–504. https://aclanthology.org/E09-1057/
Leng, J., Huang, C., Zhu, B., & Huang, J. (2025). Taming Overconfidence in LLMs: Reward Calibration in RLHF. The Thirteenth International Conference on Learning Representations (ICLR 2025). https://proceedings.iclr.cc/paper_files/paper/2025/hash/29fb6e1456b3d8b57ede5c45aa2c6537-Abstract-Conference.html
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., et al. (2020a). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.703
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., et al. (2020b). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 9459–9474. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
Li, B., Luo, J., Briakou, E., & Cherry, C. (2025c). Leveraging Domain Knowledge at Inference Time for LLM Translation: Retrieval versus Generation. Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing (KnowledgeNLP 2025), 91–106. https://doi.org/10.18653/v1/2025.knowledgenlp-1.7
Li, C., Wang, J., Zhang, Y., Zhu, K., Hou, W., Lian, J., et al. (2023). Large Language Models Understand and Can be Enhanced by Emotional Stimuli. Preprint, arXiv:2307.11760 (a short version accepted at the LLM@IJCAI 2023 workshop). https://arxiv.org/abs/2307.11760
Li, J., Li, G., Chang, Y., & Wu, Y. (2025a). Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2025. https://aclanthology.org/2025.findings-emnlp.44/
Li, K., Liu, T., Bashkansky, N., Bau, D., Viégas, F., Pfister, H., & Wattenberg, M. (2024). Measuring and Controlling Instruction (In)Stability in Language Model Dialogs. First Conference on Language Modeling (COLM 2024). https://openreview.net/forum?id=60a1SAtH4e
Li, P., Yang, J., Islam, M. A., & Ren, S. (2025d). Making AI Less 'Thirsty'. Communications of the ACM, 68(7), 54–61. https://doi.org/10.1145/3724499
Li, Z., Ji, S., & Tiedemann, J. (2026). Test-Time Scaling of Reasoning Models for Machine Translation. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 2902–2917. https://doi.org/10.18653/v1/2026.eacl-long.133
Li, Z., Shi, Y., Liu, Z., Yang, F., Payani, A., Liu, N., & Du, M. (2025b). Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages. Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28186–28194. https://doi.org/10.1609/aaai.v39i27.35038
Liang, W., Izzo, Z., Zhang, Y., Lepp, H., Cao, H., Zhao, X., et al. (2024). Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. Proceedings of the 41st International Conference on Machine Learning, 29575–29620. https://proceedings.mlr.press/v235/liang24b.html
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7). https://doi.org/10.1016/j.patter.2023.100779
Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., Yang, D., Potts, C., Manning, C. D., & Zou, J. (2025). Quantifying large language model usage in scientific papers. Nature Human Behaviour, 9(12), 2599–2609. https://doi.org/10.1038/s41562-025-02273-8
Liesenfeld, A., & Dingemanse, M. (2024). Rethinking open source generative AI: open-washing and the EU AI Act. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 1774–1787. https://doi.org/10.1145/3630106.3659005
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics, 12, 157–173. https://doi.org/10.1162/tacl_a_00638
Liu, S., Lefever, E., & Hoste, V. (2025). MariATE: Automatic Term Extraction Using Large Language Models in the Maritime Domain. Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing (RANLP 2025), 663–673. https://aclanthology.org/2025.ranlp-1.77/
Liu, X., Mireshghallah, N., Ginsburg, J. C., & Chakrabarty, T. (2026). Alignment Whack-a-Mole: Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models. Conference on Language Modeling (COLM 2026). https://openreview.net/forum?id=62zTHbtiDH
Lommel, A., Uszkoreit, H., & Burchardt, A. (2014). Multidimensional Quality Metrics (MQM): A Framework for Declaring and Describing Translation Quality Metrics. Tradumàtica: tecnologies de la traducció, 12, 455–463. https://doi.org/10.5565/rev/tradumatica.77
Longpre, S., Mahari, R., Lee, A., Lund, C., Oderinwale, H., Brannon, W., et al. (2024). Consent in Crisis: The Rapid Decline of the AI Data Commons. Advances in Neural Information Processing Systems, 37, 108042–108087. https://doi.org/10.52202/079017-3431
Loopstra Nixon LLP (2026). A Real and Substantial Connection: Ontario Court Confirms Jurisdiction in AI Copyright Case. Loopstra Nixon Insights. https://www.loopstranixon.com/insights/publication/a-real-and-substantial-connection--ontario-court-confirms-jurisdiction-in-ai-copyright-case
LTAC Global (2021). Summary of changes in ISO 30042. Introduction to TermBase eXchange (TBX). https://www.tbxinfo.net/summary-of-changes-in-iso-30042/
Lu, S., Bigoulaeva, I., Sachdeva, R., Tayyar Madabushi, H., & Gurevych, I. (2024). Are Emergent Abilities in Large Language Models just In-Context Learning?. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 5098–5139. https://doi.org/10.18653/v1/2024.acl-long.279
Luccioni, A. S., Jernite, Y., & Strubell, E. (2024). Power Hungry Processing: Watts Driving the Cost of AI Deployment?. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). https://arxiv.org/abs/2311.16863
Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2023). Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. Journal of Machine Learning Research, 24(253), 1-15. http://jmlr.org/papers/v24/23-0069.html
Luo, X., Chen, Y., Li, Q., & Bjerva, J. (2026). Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization Framework. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 33841–33861. https://doi.org/10.18653/v1/2026.acl-long.1560
Ma, I., Domingo, I., Krone-Martins, A., Baldi, P., & Lopes, C. (2025). Memorization: A Close Look at Books. Proceedings of the First Workshop on Large Language Model Memorization (L2M2), 169–182. https://doi.org/10.18653/v1/2025.l2m2-1.13
Macken, L., Lefever, E., & Hoste, V. (2013). TExSIS: Bilingual terminology extraction from parallel corpora using chunk-based alignment. Terminology, 19(1), 1–30. https://doi.org/10.1075/term.19.1.01mac
Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., et al. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness?. Cognitive Research: Principles and Implications, 9(1), 46. https://doi.org/10.1186/s41235-024-00572-8
Manning, C. D. (2011). Part-of-speech tagging from 97% to 100%: Is it time for some linguistics?. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing, CICLing 2011 (Lecture Notes in Computer Science, Vol. 6608, pp. 171–189). Springer. https://doi.org/10.1007/978-3-642-19400-9_14
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. https://nlp.stanford.edu/IR-book/information-retrieval-book.html
Marcus, M. P., Santorini, B., & Marcinkiewicz, M. A. (1993). Building a Large Annotated Corpus of English: The Penn Treebank. Computational Linguistics, 19(2), 313–330. https://aclanthology.org/J93-2004/
Maruf, S., Saleh, F., & Haffari, G. (2021). A survey on document-level neural machine translation: Methods and evaluation. ACM Computing Surveys, 54(2), article 45. https://doi.org/10.1145/3441691
Masley, A. (2025a). Do data centers only seem bad for the climate because we can see them?. andymasley.com, 6 September 2025. https://andymasley.com/writing/do-data-centers-only-seem-bad-for/
Masley, A. (2025b). For the climate, little things don't add up. andymasley.com, 14 September 2025. https://andymasley.com/writing/for-the-climate-little-things-dont/
Masley, A. (2026a). A crash course on US air pollution. andymasley.com, 18 May 2026. https://andymasley.com/writing/a-crash-course-on-us-air-pollution/
Masley, A. (2026b). How thirsty is AI?. andymasley.com, interactive visual. https://andymasley.com/visuals/water/
Masley, A. (2026c). I think the data center backlash is mostly about data centers. andymasley.com, 25 August 2026. https://andymasley.com/writing/i-think-the-data-center-backlash/
Masley, A. (2026d). Training AI models doesn't emit that much. andymasley.com, 12 April 2026. https://andymasley.com/writing/training-ai-models-doesnt-emit-that/
Masley, A. (2026e). What your chatbot use actually costs. andymasley.com, interactive calculator. https://andymasley.com/visuals/ai-prompt-footprint/
Masley, A. (2026f). Why I think panic about local impacts of data centers is just a panic. andymasley.com, 4 June 2026. https://andymasley.com/writing/why-i-think-panic-about-local-impacts/
Mathur, N., Baldwin, T., & Cohn, T. (2020). Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4984–4997. https://doi.org/10.18653/v1/2020.acl-main.448
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.173
McCallister, E., Grance, T., & Scarfone, K. (2010). Guide to Protecting the Confidentiality of Personally Identifiable Information (PII). National Institute of Standards and Technology (NIST SP 800-122). https://doi.org/10.6028/NIST.SP.800-122
McCorduck, P. (2004). Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. 2nd ed., A K Peters. https://www.routledge.com/Machines-Who-Think-A-Personal-Inquiry-into-the-History-and-Prospects-of-Artificial-Intelligence/McCorduck/p/book/9781568812052
McEnery, A. M., & Xiao, R. Z. (2007). Parallel and comparable corpora: What are they up to?. In G. Anderman & M. Rogers (Eds.), Incorporating Corpora: Translation and the Linguist (Translating Europe). Multilingual Matters. https://eprints.lancs.ac.uk/id/eprint/59/
MDN Web Docs (2025). Disjunction: |. MDN Web Docs, JavaScript reference, Regular expressions. https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Regular_expressions/Disjunction
MDN Web Docs (n.d.). Regular expressions. MDN JavaScript Guide, Mozilla. https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_expressions
Meincke, L., Mollick, E. R., Mollick, L., & Shapiro, D. (2025a). Prompting Science Report 1: Prompt Engineering is Complicated and Contingent. Wharton Generative AI Labs, technical report; preprint, arXiv:2503.04818. https://arxiv.org/abs/2503.04818
Meincke, L., Mollick, E. R., Mollick, L., & Shapiro, D. (2025b). Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting. Wharton Generative AI Labs, technical report; preprint, arXiv:2506.07142. https://arxiv.org/abs/2506.07142
Meincke, L., Mollick, E. R., Mollick, L., & Shapiro, D. (2025c). Prompting Science Report 3: I'll pay you or I'll kill you, but will you care?. Wharton Generative AI Labs, technical report; preprint, arXiv:2508.00614. https://arxiv.org/abs/2508.00614
memoQ (2026). Resource Console: translation memories. memoQ 12.4 help. https://docs.memoq.com/current/en/Workspace/resconsole-tms.html
memoQ (2026). Translation memory TMX import settings (dialog). memoQ 12.4 help. https://docs.memoq.com/current/en/Workspace/translation-memory-tmx-import-settings.html
(2024). Meta Platforms, Inc & 2 others v Motaung & 186 others. Court of Appeal at Nairobi, Civil Appeal E232 & E445 of 2023 (Consolidated), [2024] KECA 1262 (KLR). https://new.kenyalaw.org/akn/ke/judgment/keca/2024/1262/eng@20 September 2024
Metz, C. (2016). In Two Moves, AlphaGo and Lee Sedol Redefined the Future. Wired, 16 March 2016. https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Micallef, K., & Borg, C. (2025). MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP. Findings of the Association for Computational Linguistics: ACL 2025, 20505–20527. https://doi.org/10.18653/v1/2025.findings-acl.1053
Miceli, M., & Posada, J. (2022). The Data-Production Dispositif. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–37. https://doi.org/10.1145/3555561
Microsoft (2026). Azure OpenAI reasoning models. Microsoft Learn. https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/reasoning
Microsoft (2026). Regular expression language: quick reference. .NET documentation, Microsoft Learn. https://learn.microsoft.com/en-us/dotnet/standard/base-types/regular-expression-language-quick-reference
Microsoft Learn (n.d.). Exchanging localizable resources. https://learn.microsoft.com/en-us/globalization/localization/exchanging-localizable-resources
Microsoft Learn (n.d.). Localization file formats. https://learn.microsoft.com/en-us/globalization/localization/localization-file-formats
Microsoft Learn (n.d.). Maintain translation memories. https://learn.microsoft.com/en-us/globalization/localization/translation-memories
Microsoft Support (n.d.). Dictate your documents in Word. https://support.microsoft.com/en-us/office/dictate-your-documents-in-word-3876e05f-3fcc-418f-b8ab-db7ce0d11d3c
Microsoft Support (n.d.). Present with real-time, automatic captions or subtitles in PowerPoint. https://support.microsoft.com/en-us/office/present-with-real-time-automatic-captions-or-subtitles-in-powerpoint-68d20e49-aec3-456a-939d-34a79e8ddd5f
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (ICLR 2013 Workshop Track). https://arxiv.org/abs/1301.3781
Mikolov, T., Yih, W., & Zweig, G. (2013b). Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. https://aclanthology.org/N13-1090/
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022). Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 11048–11064. https://aclanthology.org/2022.emnlp-main.759/
Mistral AI (2025). Our contribution to a global environmental standard for AI. Mistral AI, 22 July 2025. https://mistral.ai/news/our-contribution-to-a-global-environmental-standard-for-ai
Modarressi, A., Deilamsalehy, H., Dernoncourt, F., Bui, T., Rossi, R. A., Yoon, S., & Schütze, H. (2025). NoLiMa: Long-Context Evaluation Beyond Literal Matching. Proceedings of the 42nd International Conference on Machine Learning, PMLR 267, 44554–44570. https://proceedings.mlr.press/v267/modarressi25a.html
Mollick, E. (2023). Centaurs and Cyborgs on the Jagged Frontier. One Useful Thing. https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged
Moorkens, J. (2011). Translation memories guarantee consistency: Truth or fiction?. Proceedings of Translating and the Computer 33 (Aslib). https://aclanthology.org/2011.tc-1.17/
Moslem, Y., Haque, R., Kelleher, J. D., & Way, A. (2023). Adaptive Machine Translation with Large Language Models. Proceedings of the 24th Annual Conference of the European Association for Machine Translation (EAMT 2023), 227–237. https://aclanthology.org/2023.eamt-1.22/
Muldoon, J., Cant, C., Wu, B., & Graham, M. (2024). A typology of artificial intelligence data work. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241232632
Müller, T., Cotterell, R., Fraser, A., & Schütze, H. (2015). Joint Lemmatization and Morphological Tagging with Lemming. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2268–2274. https://aclanthology.org/D15-1272/
Nagli, G. (2026). Hacking Moltbook: The AI Social Network Any Human Can Control. Wiz Blog. https://www.wiz.io/blog/exposed-moltbook-database-reveals-millions-of-api-keys
Naous, T., Ryan, M. J., Ritter, A., & Xu, W. (2024). Having Beer after Prayer? Measuring Cultural Bias in Large Language Models. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 16366–16393. https://doi.org/10.18653/v1/2024.acl-long.862
Nasr, M., Rando, J., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., et al. (2025). Scalable Extraction of Training Data from Aligned, Production Language Models. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). https://proceedings.iclr.cc/paper_files/paper/2025/hash/cce0e917b050208170151f77b497fc71-Abstract-Conference.html
National Institute of Standards and Technology (n.d.). Deterministic Algorithm. NIST Computer Security Resource Center Glossary. https://csrc.nist.gov/glossary/term/deterministic_algorithm
Naveen, P., & Trojovský, P. (2024). Overview and challenges of machine translation for contextually appropriate translations. iScience, 27(10), 110878. https://www.cell.com/iscience/fulltext/S2589-0042(24)02103-5
Neuberger, C., Bartsch, A., Fröhlich, R., Hanitzsch, T., Reinemann, C., & Schindler, J. (2023). The digital transformation of knowledge order: a model for the analysis of the epistemic crisis. Annals of the International Communication Association, 47(2). https://doi.org/10.1080/23808985.2023.2169950
Nghiem, H., Prindle, J., Zhao, J., & Daumé III, H. (2024). “You Gotta be a Doctor, Lin” : An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 7268–7287. https://doi.org/10.18653/v1/2024.emnlp-main.413
Nielsen, D. S. (2023). ScandEval: A Benchmark for Scandinavian Natural Language Processing. Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa 2023), 185–201. https://aclanthology.org/2023.nodalida-1.20/
NIST Big Data Public Working Group, Definitions and Taxonomies Subgroup (2019). NIST Big Data Interoperability Framework: Volume 1, Definitions (Version 3). NIST Special Publication 1500-1r2, National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1500-1r2
NLLB Team, Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M., et al. (2024). Scaling neural machine translation to 200 languages. Nature, 630, 841–846. https://doi.org/10.1038/s41586-024-07335-x
O'Hagan, M. (Ed.) (2020). The Routledge handbook of translation and technology. Routledge. https://doi.org/10.4324/9781315311258
O'Shaughnessy, D. (2025). Speaker diarization: A review of objectives and methods. Applied Sciences, 15(4), 2002. https://doi.org/10.3390/app15042002
OASIS (2025). XLIFF Version 2.2. Organization for the Advancement of Structured Information Standards, Committee Specification 01. https://www.oasis-open.org/standard/xliff-v2-2-cs01/
OECD (1999). Oligopoly: Key findings, summary and notes. OECD Competition Law and Policy Working Papers, OECD Publishing. https://doi.org/10.1787/4f85ebf0-en
OECD (2019). OECD Skills Outlook 2019: Thriving in a Digital World. OECD Publishing. https://doi.org/10.1787/df80bc12-en
OECD (2021). Methodologies to Measure Market Competition. OECD Competition Law and Policy Working Papers, OECD Publishing. https://doi.org/10.1787/29bf31c1-en
Office of the Privacy Commissioner of Canada (2026). Joint Investigation of OpenAI OpCo, LLC. PIPEDA Findings #2026-002, joint investigation with the Commission d’accès à l’information du Québec and the Information and Privacy Commissioners of British Columbia and Alberta. https://www.priv.gc.ca/en/opc-actions-and-decisions/investigations/investigations-into-businesses/2026/pipeda-2026-002/
Office québécois de la langue française (1999). extraction de données. Vitrine linguistique — Grand dictionnaire terminologique. https://vitrinelinguistique.oqlf.gouv.qc.ca/fiche-gdt/fiche/8873867/extraction-de-donnees
Office québécois de la langue française (2003). Expression rationnelle. Vitrine linguistique, Grand dictionnaire terminologique. https://vitrinelinguistique.oqlf.gouv.qc.ca/fiche-gdt/fiche/8359703/expression-rationnelle
Okapi Framework (n.d.). Open standards. https://okapiframework.org/wiki/index.php/Open_Standards
Open Source Initiative (2024). The Open Source AI Definition – 1.0. Open Source Initiative. https://opensource.org/ai/open-source-ai-definition
Open Source Initiative (2025). Open Weights: not quite what you've been told. Open Source Initiative. https://opensource.org/ai/open-weights
OpenAI (2023). Custom instructions for ChatGPT. OpenAI. https://openai.com/index/custom-instructions-for-chatgpt/
OpenAI (2023). GPT-4 Technical Report. arXiv:2303.08774 (preprint). https://arxiv.org/abs/2303.08774
OpenAI (2025). Expanding on what we missed with sycophancy. OpenAI (blog), 2 May 2025. https://openai.com/index/expanding-on-sycophancy/
OpenAI (2026). OpenAI Model Spec. Version of 18 August 2026. https://model-spec.openai.com/18 August 2026.html
OpenAI (n.d.). Function calling. OpenAI Developers (API documentation). https://developers.openai.com/api/docs/guides/function-calling
OSCAR (LISA) (2008). SRX 2.0 - OSCAR Recommendation. Localization Industry Standards Association. https://www.ttt.org/oscarStandards/srx/srx20.html
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://proceedings.neurips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html
Ouyang, S., Zhang, J. M., Harman, M., & Wang, M. (2025). An Empirical Study of the Non-Determinism of ChatGPT in Code Generation. ACM Transactions on Software Engineering and Methodology, 34(2). https://doi.org/10.1145/3697010
Pan, L., Zou, L., Guo, S., Ni, J., & Zheng, H.-T. (2026b). Natural-Language Agent Harnesses. arXiv preprint arXiv:2603.25723. https://arxiv.org/abs/2603.25723
Pan, M. Z., Yang, S., Arabzadeh, N., Chiang, W.-L., Stoica, I., & Zaharia, M. (2026a). HarnessTax: How Much Does Harness Matter for Coding Agents?. Project page, UC Berkeley and Arena; also published on the Arena blog. https://harnesstax.github.io/
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: a method for automatic evaluation of machine translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia, 311–318. https://doi.org/10.3115/1073083.1073135
Park, C. R., Heo, H., Suh, C. H., & Shim, W. H. (2025). Uncover This Tech Term: Application Programming Interface for Large Language Models. Korean Journal of Radiology. https://doi.org/10.3348/kjr.2025.0360
Pedersen, T. A., Mori, Y., Botteri, E., Engjom, T., Seip, B., Dimcevski, G. G., & Havre, R. F. (2026). Learning and deskilling effects of artificial intelligence in colonoscopy among endoscopists with different levels of experience: A pragmatic, prospective trial. Endoscopy, 58(9), 1003–1014. https://doi.org/10.1055/a-2858-7084
Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., et al. (2023). Towards Making the Most of ChatGPT for Machine Translation. Findings of the Association for Computational Linguistics: EMNLP 2023, 5622–5633. https://doi.org/10.18653/v1/2023.findings-emnlp.373
Peng, Z., Bawden, R., & Yvon, F. (2025). Investigating length issues in document-level machine translation. Proceedings of Machine Translation Summit XX: Volume 1. https://aclanthology.org/2025.mtsummit-1.3/
Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1162
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., et al. (2018). Deep Contextualized Word Representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). https://doi.org/10.18653/v1/N18-1202
Peterson, A. J. (2025). AI and the problem of knowledge collapse. AI & Society, 40(5), 3249-3269. https://doi.org/10.1007/s00146-024-02173-x
Petrov, A., La Malfa, E., Torr, P., & Bibi, A. (2023). Language Model Tokenizers Introduce Unfairness Between Languages. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://proceedings.neurips.cc/paper_files/paper/2023/hash/74bb24dca8334adce292883b4b651eda-Abstract-Conference.html
Peykani, P., Ghanidel, S., Javadi-Sisi, I., Snasel, V., & Mirjalili, S. (2026). A Holistic Review of Agentic AI Frameworks, Applications, and Research Trajectories. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-026-10675-8
Phan, L., Gatti, A., Li, N., Khoja, A., Kim, R., Ren, R., et al. (2026). A benchmark of expert-level academic questions to assess AI capabilities. Nature, 649(8099), 1139–1146. https://doi.org/10.1038/s41586-025-09962-4
Phillipson, R. (1992). Linguistic Imperialism. Oxford University Press (Oxford Applied Linguistics). https://books.google.com/books?id=4jVeGWtzQ1oC
Piantadosi, S. T., Tily, H., & Gibson, E. (2012). The communicative function of ambiguity in language. Cognition, 122(3), 280–291. https://doi.org/10.1016/j.cognition.2011.10.004
Polonioli, A. (2025). Moving LLM evaluation forward: lessons from human judgment research. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2025.1592399
Pomerenke, D., Nothnagel, J., & Ostermann, S. (2025). The AI Language Proficiency Monitor: Tracking the Progress of LLMs on Multilingual Benchmarks. Preprint, arXiv:2507.08538. https://arxiv.org/abs/2507.08538
Popel, M., Tomkova, M., Tomek, J., Kaiser, Ł., Uszkoreit, J., Bojar, O., & Žabokrtský, Z. (2020). Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals. Nature Communications, 11, 4381. https://doi.org/10.1038/s41467-020-18073-9
Popović, M. (2015). chrF: character n-gram F-score for automatic MT evaluation. Proceedings of the Tenth Workshop on Statistical Machine Translation (WMT 2015), Lisbon, 392–395. https://doi.org/10.18653/v1/W15-3049
Popović, M. (2017). chrF++: words helping character n-grams. Proceedings of the Second Conference on Machine Translation (WMT 2017), Copenhagen, 612–618. https://doi.org/10.18653/v1/W17-4770
Porter, B., & Machery, E. (2024). AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably. Scientific Reports, 14, 26133. https://doi.org/10.1038/s41598-024-76900-1
Post, M. (2018). A Call for Clarity in Reporting BLEU Scores. Proceedings of the Third Conference on Machine Translation: Research Papers (WMT 2018), 186–191. https://doi.org/10.18653/v1/W18-6319
Qin, Y., Zhang, T., Zhang, T., Shen, Y., Luo, W., Sun, H., et al. (2025). SysBench: Can LLMs Follow System Message?. The Thirteenth International Conference on Learning Representations (ICLR 2025). https://mlanthology.org/iclr/2025/qin2025iclr-sysbench/
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large-scale weak supervision. Proceedings of the 40th International Conference on Machine Learning (PMLR 202), 28492–28518. https://proceedings.mlr.press/v202/radford23a.html
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI (preprint). https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://proceedings.neurips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html
Rajaee, S., Vincent, S., Berard, A., Fadaee, M., Marchisio, K., & Kocmi, T. (2026). Unlocking Reasoning Capability on Machine Translation in Large Language Models. Preprint, arXiv:2602.14763. https://arxiv.org/abs/2602.14763
Raji, D., Denton, E., Bender, E. M., Hanna, A., & Paullada, A. (2021). AI and the Everything in the Whole Wide World Benchmark. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 1 (NeurIPS 2021). https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/084b6fbb10729ed4da8c3d3f5a3ae7c9-Abstract-round2.html
Ramnath, K., Zhou, K., Guan, S., Mishra, S. S., Qi, X., Shen, Z., et al. (2025). A systematic survey of automatic prompt optimization techniques. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2025.emnlp-main.1681
Ramos, M. M., Fernandes, P., Agrawal, S., & Martins, A. F. T. (2025). Multilingual contextualization of large language models for document-level machine translation. Second Conference on Language Modeling (COLM 2025). https://openreview.net/forum?id=Ah0U1r5Ldq
Raschka, S. (2024). Machine Learning Q and AI. No Starch Press. https://nostarch.com/machine-learning-q-and-ai
Raya, R. (2004). XML in localisation: Use XLIFF to translate documents. Maxprograms. https://www.maxprograms.com/articles/xliff.html
Raya, R. (2005). XML in localisation: Reuse translations with TM and TMX. Maxprograms. https://www.maxprograms.com/articles/tmx.html
Rayson, P., & Garside, R. (2000). Comparing Corpora using Frequency Profiling. Proceedings of the Workshop on Comparing Corpora, 38th Annual Meeting of the Association for Computational Linguistics, 1–6. https://aclanthology.org/W00-0901/
Rebholz-Schuhmann, D., Jimeno Yepes, A. J., van Mulligen, E. M., Kang, N., Kors, J., Milward, D., et al. (2010). The CALBC Silver Standard Corpus for Biomedical Named Entities — A Study in Harmonizing the Contributions from Four Independent Named Entity Taggers. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10). https://aclanthology.org/L10-1609/
Rei, R., Stewart, C., Farinha, A. C., & Lavie, A. (2020). COMET: A Neural Framework for MT Evaluation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2685–2702. https://doi.org/10.18653/v1/2020.emnlp-main.213
Rei, R., Treviso, M., Guerreiro, N. M., Zerva, C., Farinha, A. C., Maroti, C., de Souza, J. G. C., Glushkova, T., Alves, D., Lavie, A., Coheur, L., & Martins, A. F. T. (2022). CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task. Proceedings of the Seventh Conference on Machine Translation (WMT 2022), Abu Dhabi, 634–645. https://doi.org/10.18653/v1/2022.wmt-1.60
Reuters (2016). Striking Back Against the Machine: Korean Go Player Beats Google Program. Fortune, 13 March 2016. https://fortune.com/2016/03/13/korean-player-beats-alphago/
Rigouts Terryn, A. (2021a). ACTER terminology annotation guidelines. Ghent University, LT3 Language and Translation Technology Team. http://hdl.handle.net/1854/LU-8503113
Rigouts Terryn, A. (2026). Large Language Models Are Good Term Extractors: A Systematic Evaluation. Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), 7631–7643. https://doi.org/10.63317/49gta5itne3q
Rigouts Terryn, A., & de Lhoneux, M. (2024). Exploratory Study on the Impact of English Bias of Generative Large Language Models in Dutch and French. Proceedings of the Fourth Workshop on Human Evaluation of NLP Systems (HumEval) @ LREC-COLING 2024, 12–27. https://aclanthology.org/2024.humeval-1.2/
Rigouts Terryn, A., Drouin, P., Hoste, V., & Lefever, E. (2019). Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), 1012–1021. https://doi.org/10.26615/978-954-452-056-4_117
Rigouts Terryn, A., Hoste, V., & Lefever, E. (2020a). In No Uncertain Terms: A Dataset for Monolingual and Multilingual Automatic Term Extraction from Comparable Corpora. Language Resources and Evaluation, 54(2), 385–418. https://doi.org/10.1007/s10579-019-09453-9
Rigouts Terryn, A., Hoste, V., & Lefever, E. (2021). HAMLET: Hybrid Adaptable Machine Learning approach to Extract Terminology. Terminology, 27(2), 254–293. https://doi.org/10.1075/term.20017.rig
Rigouts Terryn, A., Hoste, V., & Lefever, E. (2022a). D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction. Proceedings of the Workshop on Terminology in the 21st century: many faces, many places (TERM21), 33–40. https://aclanthology.org/2022.term-1.7/
Rigouts Terryn, A., Hoste, V., & Lefever, E. (2022b). Tagging Terms in Text: A Supervised Sequential Labelling Approach to Automatic Term Extraction. Terminology, 28(1), 157–189. https://doi.org/10.1075/term.21010.rig
Rigouts Terryn, A., Hoste, V., Drouin, P., & Lefever, E. (2020b). TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset. Proceedings of the 6th International Workshop on Computational Terminology (CompuTerm 2020), 85–94. https://aclanthology.org/2020.computerm-1.12/
Rincé, S., & Banse, A. (2025). EcoLogits: Evaluating the Environmental Impacts of Generative AI. Journal of Open Source Software, 10(111), 7471. https://doi.org/10.21105/joss.07471
Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613(16)30098-5
Rivas Ginel, M. I., & Moorkens, J. (2025). Translators' trust and distrust in the times of GenAI. Translation Studies, 18, 283–299. https://doi.org/10.1080/14781700.2025.2507594
Roberts, S. T. (2019). Behind the Screen: Content Moderation in the Shadows of Social Media. Yale University Press. https://doi.org/10.2307/j.ctvhrcz0v
Rollet, C. (2025). Scale AI is facing a third worker lawsuit in about a month. TechCrunch, 22 January 2025. https://techcrunch.com/2025/01/22/scale-ai-is-facing-a-third-worker-lawsuit-in-about-a-month/
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. https://doi.org/10.1037/h0042519
Rothwell, A., Moorkens, J., Fernández-Parra, M., Drugan, J., & Austermuehl, F. (2023). Translation Tools and Technologies. Routledge. https://doi.org/10.4324/9781003160793
Röttger, P., Kirk, H. R., Vidgen, B., Attanasio, G., Bianchi, F., & Hovy, D. (2024). XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 5377–5400. https://aclanthology.org/2024.naacl-long.301/
Roukos, S., Graff, D., & Melamed, D. (1995). Hansard French/English (LDC95T20). Linguistic Data Consortium. https://catalog.ldc.upenn.edu/LDC95T20
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x
Russell, J. (2016). Google AI beats Go world champion again to complete historic 4-1 series victory. TechCrunch, 15 March 2016. https://techcrunch.com/2016/03/15/google-ai-beats-go-world-champion-again-to-complete-historic-4-1-series-victory/
Russell, J., Karpinska, M., & Iyyer, M. (2025). People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 5342–5373. https://doi.org/10.18653/v1/2025.acl-long.267
Sager, P. J., Meyer, B., Yan, P., von Wartburg-Kottler, R., Etaiwi, L., Enayati, A., et al. (2026). A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.1.19490
Sainz, O., Campos, J., García-Ferrero, I., Etxaniz, J., Lopez de Lacalle, O., & Agirre, E. (2023). NLP Evaluation in Trouble: On the Need to Measure LLM Data Contamination for Each Benchmark. Findings of the Association for Computational Linguistics: EMNLP 2023, 10776–10787. https://doi.org/10.18653/v1/2023.findings-emnlp.722
Sajadieh, S., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Santarlasci, L., Pava, J., Maslej, N., et al. (2026). The AI Index 2026 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. https://hai.stanford.edu/ai-index/2026-ai-index-report
Salewski, L., Alaniz, S., Rio-Torto, I., Schulz, E., & Akata, Z. (2023). In-Context Impersonation Reveals Large Language Models' Strengths and Biases. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://openreview.net/forum?id=CbsJ53LdKc
Salvi del Pero, A., Wyckoff, P., & Vourc'h, A. (2022). Using Artificial Intelligence in the workplace: What are the main ethical risks?. OECD Social, Employment and Migration Working Papers, No. 273, OECD Publishing. https://doi.org/10.1787/840a2d9f-en
Salvi, F., Horta Ribeiro, M., Gallotti, R., & West, R. (2025). On the conversational persuasiveness of GPT-4. Nature Human Behaviour, 9(8), 1645–1653. https://doi.org/10.1038/s41562-025-02194-6
(2024). Samasource EPZ Limited t/a Sama v Meta Platforms, Incorporated & 186 others. Court of Appeal at Nairobi, Civil Appeal E595 of 2023 (consolidated with E602 and E615 of 2023), [2024] KECA 1152 (KLR). https://new.kenyalaw.org/akn/ke/judgment/keca/2024/1152/eng@20 September 2024
(2024). Samuelson-Glushko Canadian Internet Policy and Public Interest Clinic (CIPPIC) v. Sahni. Federal Court (Canada), T-1717-24. https://www.cippic.ca/our-work/cippic-v-sahni
Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., & Hashimoto, T. (2023). Whose Opinions Do Language Models Reflect?. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 29971–30004. https://proceedings.mlr.press/v202/santurkar23a.html
Scarfe, P., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A "Turing Test" case study. PLOS ONE, 19(6), e0305354. https://doi.org/10.1371/journal.pone.0305354
Schaeffer, R., Miranda, B., & Koyejo, S. (2023). Are Emergent Abilities of Large Language Models a Mirage?. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://proceedings.neurips.cc/paper_files/paper/2023/hash/adc98a266f45005c403b8311ca7e8bd7-Abstract-Conference.html
Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Hambro, E., et al. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://proceedings.neurips.cc/paper_files/paper/2023/hash/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html
Schmidtová, P., Bafna, N., Aycock, S., Vico, G., Kamzela, W., Hämmerl, K., & Zouhar, V. (2026). How Important is 'Perfect' English for Machine Translation Prompts?. Findings of the Association for Computational Linguistics: EACL 2026, 760–777. https://doi.org/10.18653/v1/2026.findings-eacl.38
Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., et al. (2024). The Prompt Report: A Systematic Survey of Prompt Engineering Techniques. arXiv:2406.06608 (preprint). https://arxiv.org/abs/2406.06608
Schut, L., Gal, Y., & Farquhar, S. (2025). Do Multilingual LLMs Think In English?. ICLR 2025 Workshop on Building Trust in Language Models and Applications. https://openreview.net/forum?id=I8BOtOPcOv
Schwartz, R., Vassilev, A., Greene, K. K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence. NIST Special Publication 1270, National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1270
Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2024). Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting. Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024). https://arxiv.org/abs/2310.11324
Sellam, T., Das, D., & Parikh, A. P. (2020). BLEURT: Learning Robust Metrics for Text Generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 7881–7892. https://doi.org/10.18653/v1/2020.acl-main.704
Senger, E., Campbell, Y., van der Goot, R., & Plank, B. (2025). Crossing Domains without Labels: Distant Supervision for Term Extraction. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, 1366–1378. https://aclanthology.org/2025.emnlp-industry.95/
Sennrich, R., Haddow, B., & Birch, A. (2016). Neural Machine Translation of Rare Words with Subword Units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/P16-1162
Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., et al. (2024). Towards Understanding Sycophancy in Language Models. Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024). https://openreview.net/forum?id=tvhaxkMKAn
Sharoff, S., Rapp, R., & Zweigenbaum, P. (2023). Building and Using Comparable Corpora for Multilingual Natural Language Processing. Synthesis Lectures on Human Language Technologies. Springer. https://doi.org/10.1007/978-3-031-31384-4
Shi, Z., Wei, J., Xu, Z., & Liang, Y. (2024). Why Larger Language Models Do In-context Learning Differently?. Proceedings of the 41st International Conference on Machine Learning (ICML 2024), PMLR 235, 44991–45013. https://proceedings.mlr.press/v235/shi24f.html
Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631. https://doi.org/10.1038/s41586-024-07566-y
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144. https://doi.org/10.1126/science.aar6404
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. https://doi.org/10.1038/nature24270
Sinclair, J. (2004). Corpus and Text: Basic Principles. In M. Wynne (Ed.), Developing Linguistic Corpora: a Guide to Good Practice. AHDS Literature, Languages and Linguistics. https://users.ox.ac.uk/~martinw/dlc/chapter1.htm
Singh, S., Nan, Y., Wang, A., D'Souza, D., Kapoor, S., Üstün, A., et al. (2025b). The Leaderboard Illusion. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), Datasets and Benchmarks Track. https://openreview.net/forum?id=4Ae8edNqm0
Singh, S., Romanou, A., Fourrier, C., Adelani, D. I., Ngui, J. G., Vila-Suero, D., et al. (2025a). Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 18761–18799. https://doi.org/10.18653/v1/2025.acl-long.919
Singhal, P., Goyal, T., Xu, J., & Durrett, G. (2024). A Long Way to Go: Investigating Length Correlations in RLHF. First Conference on Language Modeling (COLM 2024). https://openreview.net/forum?id=G8LaO1P0xv
Sketch Engine (2020). Gutenberg Corpora 2020. sketchengine.eu. https://www.sketchengine.eu/gutenberg-corpora-2020/
Sketch Engine (n.d.). frTenTen: French corpus from the web. sketchengine.eu. https://www.sketchengine.eu/frtenten-french-corpus/
Snell, C., Lee, J., Xu, K., & Kumar, A. (2025). Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). https://proceedings.iclr.cc/paper_files/paper/2025/hash/1b623663fd9b874366f3ce019fdfdd44-Abstract-Conference.html
Society of Authors (2024). SoA survey reveals a third of translators and quarter of illustrators losing work to AI. The Society of Authors, news, 11 April 2024. https://societyofauthors.org/2024/04/11/soa-survey-reveals-a-third-of-translators-and-quarter-of-illustrators-losing-work-to-ai/
Sorrell, S. (2009). Jevons' Paradox revisited: The evidence for backfire from improved energy efficiency. Energy Policy, 37(4), 1456–1469. https://doi.org/10.1016/j.enpol.2008.12.003
Sourati, Z., Ziabari, A. S., & Dehghani, M. (2026). The homogenizing effect of large language models on human expression and thought. Trends in Cognitive Sciences, 30(9), 805–816. https://doi.org/10.1016/j.tics.2026.01.003
Spence, R., Bifulco, A., Bradbury, P., Martellozzo, E., & DeMarco, J. (2024). Content Moderator Mental Health, Secondary Trauma, and Well-being: A Cross-Sectional Study. Cyberpsychology, Behavior, and Social Networking, 27(2), 149–155. https://doi.org/10.1089/cyber.2023.0298
Sprague, Z., Yin, F., Rodriguez, J. D., Jiang, D., Wadhwa, M., Singhal, P., et al. (2025). To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). https://proceedings.iclr.cc/paper_files/paper/2025/hash/ead542f13a38179d1b55b88610f959a1-Abstract-Conference.html
Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386. https://www.sciencedirect.com/science/article/pii/S0747563224002541
Stanford HAI (n.d.). What is a Context Window?. Stanford HAI, AI definitions. https://hai.stanford.edu/ai-definitions/what-is-a-context-window
Stanford HAI (n.d.). What is an Open-Weight Model?. Stanford HAI, AI definitions. https://hai.stanford.edu/ai-definitions/what-is-an-open-weight-model
Stanford HAI (n.d.). What is Attention Mechanism?. Stanford HAI, AI definitions. https://hai.stanford.edu/ai-definitions/what-is-attention-mechanism
Steiger, M., Bharucha, T. J., Venkatagiri, S., Riedl, M. J., & Lease, M. (2021). The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3411764.3445092
(2025). Stephen Thaler v. Shira Perlmutter. United States Court of Appeals for the District of Columbia Circuit, 23-5233. https://law.justia.com/cases/federal/appellate-courts/cadc/23-5233/23-5233-2025-03-18.html
Steyvers, M., Tejeda, H., Kumar, A., Belem, C., Karny, S., Hu, X., Mayer, L. W., & Smyth, P. (2025). What large language models know and what people think they know. Nature Machine Intelligence, 7(2), 221–231. https://doi.org/10.1038/s42256-024-00976-7
Strathern, M. (1997). 'Improving ratings': audit in the British University system. European Review, 5(3), 305–321. https://doi.org/10.1002/(SICI)1234-981X(199707)5:3<305::AID-EURO184>3.0.CO;2-4
Suresh, H., & Guttag, J. V. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21). https://doi.org/10.1145/3465416.3483305
Sutton, R. S. (2019). The bitter lesson. Incomplete Ideas (blog). http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1
Tam, Z. R., Wu, C.-K., Tsai, Y.-L., Lin, C.-Y., Lee, H., & Chen, Y.-N. (2024). Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 1218–1236. https://doi.org/10.18653/v1/2024.emnlp-industry.91
Tan, Z., Li, D., Wang, S., Beigi, A., Jiang, B., Bhattacharjee, A., et al. (2024). Large Language Models for Data Annotation and Synthesis: A Survey. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 930–957. https://aclanthology.org/2024.emnlp-main.54/
Tanzer, G., Suzgun, M., Visser, E., Jurafsky, D., & Melas-Kyriazi, L. (2024). A Benchmark for Learning to Translate a New Language from One Grammar Book. Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024). https://proceedings.iclr.cc/paper_files/paper/2024/hash/52d63f9e4b81f866bf69fb3c834aad47-Abstract-Conference.html
Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9), pgae346. https://doi.org/10.1093/pnasnexus/pgae346
Teutloff, O., Einsiedler, J., Kässi, O., Braesemann, F., Mishkin, P., & del Rio-Chanona, R. M. (2025). Winners and losers of generative AI: Early evidence of shifts in freelancer demand. Journal of Economic Behavior & Organization, 235, 106845. https://doi.org/10.1016/j.jebo.2024.106845
Thai, K., Karpinska, M., Krishna, K., Ray, B., Inghilleri, M., Wieting, J., & Iyyer, M. (2022). Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature. Proceedings of EMNLP 2022, 9882–9902. https://doi.org/10.18653/v1/2022.emnlp-main.672
(2026). Thaler v. Perlmutter. Supreme Court of the United States, No. 25-449, certiorari denied. https://www.supremecourt.gov/search.aspx?filename=/docket/docketfiles/html/public/25-449.html
(2023). The New York Times Company v. Microsoft Corporation. United States District Court for the Southern District of New York, 1:23-cv-11195. https://www.courtlistener.com/docket/68117049/the-new-york-times-company-v-microsoft-corporation/
Thompson, B., Dhaliwal, M., Frisch, P., Domhan, T., & Federico, M. (2024). A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism. Findings of the Association for Computational Linguistics: ACL 2024, 1763–1775. https://doi.org/10.18653/v1/2024.findings-acl.103
(2025). Thomson Reuters Enterprise Centre GmbH v. ROSS Intelligence Inc.. United States District Court for the District of Delaware. https://www.courtlistener.com/docket/17131648/thomson-reuters-enterprise-centre-gmbh-v-ross-intelligence-inc/
Tiedemann, J. (2012). Parallel Data, Tools and Interfaces in OPUS. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), Istanbul, 2214–2218. https://aclanthology.org/L12-1246/
Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S. (2025). Working with AI: Measuring the Applicability of Generative AI to Occupations. arXiv preprint arXiv:2507.07935. http://arxiv.org/abs/2507.07935
Toral, A., Castilho, S., Hu, K., & Way, A. (2018). Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation. Proceedings of the Third Conference on Machine Translation: Research Papers, 113–123. https://doi.org/10.18653/v1/W18-6312
(2025). Toronto Star Newspapers Limited v. OpenAI Inc.. Ontario Superior Court of Justice, CV-24-00732231-00CL, 2025 ONSC 6217. https://www.canlii.org/en/on/onsc/doc/2025/2025onsc6217/2025onsc6217.html
Torrielli, F., Locci, S., Rapp, A., & Di Caro, L. (2026). Exploiting large language models in peer review: indirect prompt injection attacks and integrity probes. Scientometrics, 131(7), 4889–4951. https://doi.org/10.1007/s11192-026-05695-x
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., et al. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv:2307.09288 (preprint). https://arxiv.org/abs/2307.09288
Tran, H. T. H., González-Gallardo, C.-E., Delaunay, J., Doucet, A., & Pollak, S. (2024). Is Prompting What Term Extraction Needs?. In Text, Speech, and Dialogue (TSD 2024), Lecture Notes in Computer Science (pp. 17–29). Springer. https://doi.org/10.1007/978-3-031-70563-2_2
Tran, H. T. H., González-Gallardo, C.-E., Doucet, A., & Pollak, S. (2025). LlamATE: Automated terminology extraction using large-scale generative language models. Terminology, 31(1), 5–36. https://doi.org/10.1075/term.00082.tra
Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://openreview.net/forum?id=bzs4uPLXvi
U.S. Copyright Office (2025). Copyright and Artificial Intelligence, Part 2: Copyrightability. Report of the Register of Copyrights. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf
U.S. Environmental Protection Agency (2026). Greenhouse Gas Equivalencies Calculator - Calculations and References. U.S. Environmental Protection Agency, last updated 4 August 2026. https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references
Unicode Consortium (n.d.). Glossary of Unicode Terms. The Unicode Consortium. https://www.unicode.org/glossary/
Unicode Consortium (n.d.). The Unicode Standard: A Technical Introduction. https://www.unicode.org/standard/principles.html
United States (1976). Copyright Act of 1976, 17 U.S.C. § 107 (Limitations on exclusive rights: Fair use). United States Code. https://uscode.house.gov/view.xhtml?req=granuleid:USC-prelim-title17-section107&num=0&edition=prelim
Van Brussel, L., Tezcan, A., & Macken, L. (2018). A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch. Proceedings of LREC 2018. https://aclanthology.org/L18-1600/
Van Vlasselaer, M., Van Droogenbroeck, F., & Spruyt, B. (2026). Who wrote this? Evaluating the reliability of AI detection tools in higher education. International Journal for Educational Integrity, 22(1), 16. https://doi.org/10.1007/s40979-026-00226-w
Vanmassenhove, E., Shterionov, D., & Gwilliam, M. (2021). Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2203–2213. https://doi.org/10.18653/v1/2021.eacl-main.188
Vanmassenhove, E., Shterionov, D., & Way, A. (2019). Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation. Proceedings of Machine Translation Summit XVII: Research Track, 222–232. https://aclanthology.org/W19-6622/
Vanroy, B., Tezcan, A., & Macken, L. (2023). MATEO: MAchine Translation Evaluation Online. Proceedings of the 24th Annual Conference of the European Association for Machine Translation (EAMT 2023), Tampere, 499–500. https://aclanthology.org/2023.eamt-1.52/
Vassilev, A., Oprea, A., Fordyce, A., Anderson, H., Davies, X., & Hamin, M. (2025). Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations. National Institute of Standards and Technology (NIST AI 100-2 E2025). https://doi.org/10.6028/NIST.AI.100-2e2025
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems 30 (NIPS 2017). https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Vig, J. (2019). A Multiscale Visualization of Attention in the Transformer Model. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 37–42. https://aclanthology.org/P19-3007/
Villalobos, P., Ho, A., Sevilla, J., Besiroglu, T., Heim, L., & Hobbhahn, M. (2024). Position: Will we run out of data? Limits of LLM scaling based on human-generated data. Proceedings of the 41st International Conference on Machine Learning, 49523–49544. https://proceedings.mlr.press/v235/villalobos24a.html
Voita, E., Sennrich, R., & Titov, I. (2019). When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion. Proceedings of the 57th Annual Meeting of the ACL, 1198–1212. https://aclanthology.org/P19-1116/
von Ahn, L., Maurer, B., McMillen, C., Abraham, D., & Blum, M. (2008). reCAPTCHA: Human-Based Character Recognition via Web Security Measures. Science, 321(5895), 1465–1468. https://doi.org/10.1126/science.1160379
W3C (2002). Localization-Related Formats. https://www.w3.org/2002/02/01-i18n-workshop/LocFormats
W3C (2008). Extensible Markup Language (XML) 1.0 (Fifth Edition). W3C Recommendation. https://www.w3.org/TR/xml/
W3C (2009). Namespaces in XML 1.0 (Third Edition). W3C Recommendation. https://www.w3.org/TR/xml-names/
W3C (n.d.). Character encodings for beginners. W3C Internationalization. https://www.w3.org/International/questions/qa-what-is-encoding
W3C (n.d.). Choosing & applying a character encoding. W3C Internationalization. https://www.w3.org/International/questions/qa-choosing-encodings
Walters, W. H., & Wilder, E. I. (2023). Fabrication and errors in the bibliographic citations generated by ChatGPT. Scientific Reports, 13, 14045. https://doi.org/10.1038/s41598-023-41032-5
Wan, A., Klyman, K., Kapoor, S., Maslej, N., Longpre, S., Xiong, B., et al. (2025). The 2025 Foundation Model Transparency Index. arXiv:2512.10169 (preprint). https://doi.org/10.48550/arXiv.2512.10169
Wang, G., Sun, Z., Ye, S., Gong, Z., Chen, Y., Zhao, Y., Liang, Q., & Hao, D. (2026a). Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?. ACM Transactions on Software Engineering and Methodology, 35(8), 1–33. https://dl.acm.org/doi/10.1145/3771933
Wang, J., Ming, Y., Shi, Z., Vineet, V., Wang, X., Li, Y., & Joshi, N. (2024b). Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 75392–75421. https://doi.org/10.52202/079017-2400
Wang, L., & Sun, S. (2023). Dictating translations with automatic speech recognition: Effects on translators' performance. Frontiers in Psychology, 14, 1108898. https://doi.org/10.3389/fpsyg.2023.1108898
Wang, L., Sun, S., Wang, X., Gu, J., & Liu, K. (2026b). Workflow matters: Comparing human translators and multi-agent LLMs in literary translation. Target. International Journal of Translation Studies, 38(3), 422–453. https://doi.org/10.1075/target.25081.wan
Wang, X., Bloch, J., Shao, Z., Hu, Y., Zhou, S., & Gong, N. Z. (2025). WebInject: Prompt Injection Attack to Web Agents. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), 2010–2030. https://doi.org/10.18653/v1/2025.emnlp-main.104
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2023). Self-Instruct: Aligning Language Models with Self-Generated Instructions. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 13484–13508. https://aclanthology.org/2023.acl-long.754/
Wang, Y., Wang, M., Manzoor, M. A., Liu, F., Georgiev, G. N., Das, R. J., et al. (2024a). Factuality of Large Language Models: A Survey. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. https://aclanthology.org/2024.emnlp-main.1088/
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., et al. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26. https://doi.org/10.1007/s40979-023-00146-z
Wei, A., Haghtalab, N., & Steinhardt, J. (2023a). Jailbroken: How Does LLM Safety Training Fail?. Advances in Neural Information Processing Systems 36, 80079–80110. https://proceedings.neurips.cc/paper_files/paper/2023/hash/fd6613131889a4b656206c50a8bd7790-Abstract-Conference.html
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2022). Finetuned Language Models Are Zero-Shot Learners. International Conference on Learning Representations (ICLR 2022). https://openreview.net/forum?id=gEZrGCozdqR
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., et al. (2022b). Emergent Abilities of Large Language Models. Transactions on Machine Learning Research. https://openreview.net/forum?id=yzkSU5zdwD
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., et al. (2022a). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract.html
Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., et al. (2023b). Larger language models do in-context learning differently. Preprint, arXiv:2303.03846 (submitted to ICLR 2024). https://openreview.net/forum?id=DRGnEkbiQZ
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
Wendler, C., Veselovsky, V., Monea, G., & West, R. (2024). Do Llamas Work in English? On the Latent Language of Multilingual Transformers. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 15366–15394. https://doi.org/10.18653/v1/2024.acl-long.820
WHATWG (2026). HTML Standard. Living Standard. https://html.spec.whatwg.org/multipage/
Widder, D. G., Whittaker, M., & West, S. M. (2024). Why ‘open’ AI systems are actually closed, and why this matters. Nature, 635(8040), 827–833. https://doi.org/10.1038/s41586-024-08141-1
Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., et al. (2022). Sustainable AI: Environmental Implications, Challenges and Opportunities. Proceedings of Machine Learning and Systems 4 (MLSys 2022). https://proceedings.mlsys.org/paper_files/paper/2022/hash/462211f67c7d858f663355eff93b745e-Abstract.html
Wu, D., Aycock, S., & Monz, C. (2025b). Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), 20424–20440. https://doi.org/10.18653/v1/2025.emnlp-main.1031
Wu, M., Wang, W., Liu, S., Yin, H., Wang, X., Zhao, Y., Lyu, C., Wang, L., Luo, W., & Zhang, K. (2025c). The Bitter Lesson Learned from 2,000+ Multilingual Benchmarks. arXiv preprint 2504.15521. https://arxiv.org/abs/2504.15521
Wu, M., Xu, J., Yuan, Y., Haffari, G., Wang, L., Luo, W., & Zhang, K. (2025a). (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts. Transactions of the Association for Computational Linguistics, 13, 901–922. https://doi.org/10.1162/TACL.a.25
Wu, W.-C., Wei, S.-L., Huang, H.-H., & Chen, H.-H. (2026). No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs. Findings of the Association for Computational Linguistics: ACL 2026, 37404–37423. https://doi.org/10.18653/v1/2026.findings-acl.1864
Xie, T., Yuan, M., Zhang, D., Xiong, X., Shen, Z., Zhou, Z., et al. (2025). Introducing OSWorld-Verified. XLANG Lab. https://xlang.ai/blog/osworld-verified
Xie, T., Zhang, D., Chen, J., Li, X., Zhao, S., Cao, R., et al. (2024). OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 52040–52094. https://doi.org/10.52202/079017-1650
Xiong, M., Hu, Z., Lu, X., Li, Y., Fu, J., He, J., & Hooi, B. (2024). Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs. The Twelfth International Conference on Learning Representations (ICLR 2024). https://openreview.net/forum?id=gjeQKFxFpZ
Xu, Y., Hu, L., Zhao, J., Qiu, Z., Xu, K., Ye, Y., & Gu, H. (2025). A survey on multilingual large language models: corpora, alignment, and bias. Frontiers of Computer Science, 19(11), 1911362. https://doi.org/10.1007/s11704-024-40579-4
Xu, Z., Jain, S., & Kankanhalli, M. (2024). Hallucination is Inevitable: An Innate Limitation of Large Language Models. arXiv:2401.11817 (preprint). https://arxiv.org/abs/2401.11817
Yakura, H., Lopez-Lopez, E., Brinkmann, L., Serna, I., Gupta, P., Soraperra, I., & Rahwan, I. (2025). Empirical evidence of Large Language Model's influence on human spoken communication. arXiv preprint 2409.01754. https://arxiv.org/abs/2409.01754
Yang, A., Li, A., Yang, B., et al. (Qwen Team) (2025b). Qwen3 Technical Report. arXiv:2505.09388 (preprint). https://arxiv.org/abs/2505.09388
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., & Chen, X. (2024a). Large Language Models as Optimizers. The Twelfth International Conference on Learning Representations (ICLR 2024). https://proceedings.iclr.cc/paper_files/paper/2024/hash/3339f19c5fcee3ad74502947a32be9e6-Abstract-Conference.html
Yang, H., Hu, Y., Kang, S., Lin, Z., & Zhang, M. (2025a). Number Cookbook: Number Understanding of Language Models and How to Improve It. The Thirteenth International Conference on Learning Representations (ICLR 2025). https://openreview.net/forum?id=BWS5gVjgeY
Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K. R., & Press, O. (2024b). SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering. Advances in Neural Information Processing Systems 37 (NeurIPS 2024). https://proceedings.neurips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. The Eleventh International Conference on Learning Representations (ICLR 2023). https://arxiv.org/abs/2210.03629
Ye, Y., Fu, B., Huang, C., Chen, Y., & Shi, X. (2025). How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation. Preprint, arXiv:2505.19987. https://arxiv.org/abs/2505.19987
Yin, Z., Wang, H., Horio, K., Kawahara, D., & Sekine, S. (2024). Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance. Proceedings of the Second Workshop on Social Influence in Conversations (SICon 2024), 9–35. https://doi.org/10.18653/v1/2024.sicon-1.2
Yoo, H., Yang, Y., & Lee, H. (2025). Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 13392–13413. https://doi.org/10.18653/v1/2025.acl-long.657
Young, R. (2026). What Is the Alignment Tax?. arXiv:2603.00047 (preprint, v2 of 3 March 2026). https://arxiv.org/abs/2603.00047
Yuan, M., Zhou, Z., Xiong, X., Wu, W., Sun, J., Song, J., et al. (2026). OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks. arXiv preprint arXiv:2606.29537. https://doi.org/10.48550/arXiv.2606.29537
Yuan, R., Sun, S., Li, Y., Wang, Z., Cao, Z., & Li, W. (2025). Personalized Large Language Model Assistant with Evolving Conditional Memory. Proceedings of the 31st International Conference on Computational Linguistics. https://aclanthology.org/2025.coling-main.254/
Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny Can't Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), article 437, 1–21. https://doi.org/10.1145/3544548.3581388
Zebaze, A., Bawden, R., & Sagot, B. (2025). LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens. Preprint, arXiv:2510.11919. https://arxiv.org/abs/2510.11919
Zekpa, N., & Peter, A. (7 January 2025). Evaluate large language models for your machine translation tasks on AWS. AWS Machine Learning Blog. https://aws.amazon.com/blogs/machine-learning/evaluate-large-language-models-for-your-machine-translation-tasks-on-aws/
Zhang, H., Diao, S., Lin, Y., Fung, Y. R., Lian, Q., Wang, X., Chen, Y., Ji, H., & Zhang, T. (2024). R-Tuning: Instructing Large Language Models to Say `I Don't Know'. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 7113–7139. https://doi.org/10.18653/v1/2024.naacl-long.394
Zhang, J., Sleight, H., Peng, A., Schulman, J., & Durmus, E. (2025c). Stress-Testing Model Specs Reveals Character Differences among Language Models. arXiv:2510.07686 (preprint). https://arxiv.org/abs/2510.07686
Zhang, R., Zhao, W., & Eger, S. (2025a). How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs. Proceedings of NAACL 2025 (Long Papers), 10961–10988. https://doi.org/10.18653/v1/2025.naacl-long.548
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020). BERTScore: Evaluating Text Generation with BERT. Eighth International Conference on Learning Representations (ICLR 2020). http://arxiv.org/abs/1904.09675
Zhang, Z., Li, S., Zhang, Z., Liu, X., Jiang, H., Tang, X., et al. (2025b). IHEval: Evaluating Language Models on Following the Instruction Hierarchy. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025), 8374–8398. https://doi.org/10.18653/v1/2025.naacl-long.425
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017). Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/D17-1323
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., et al. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Datasets and Benchmarks Track. https://proceedings.neurips.cc/paper_files/paper/2023/hash/91f18a1287b398d378ef22505bf41832-Abstract-Datasets_and_Benchmarks.html
Zheng, M., Pei, J., Logeswaran, L., Lee, M., & Jurgens, D. (2024). When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2024, 15126–15154. https://aclanthology.org/2024.findings-emnlp.888/
Zhong, W., Guo, L., Gao, Q., Ye, H., & Wang, Y. (2024). MemoryBank: Enhancing Large Language Models with Long-Term Memory. Proceedings of the AAAI Conference on Artificial Intelligence, 38(17). https://doi.org/10.1609/aaai.v38i17.29946
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., et al. (2023). LIMA: Less Is More for Alignment. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://openreview.net/forum?id=KBMOKmX2he
Zhuo, J., Zhang, S., Fang, X., Duan, H., Lin, D., & Chen, K. (2024). ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs. Findings of the Association for Computational Linguistics: EMNLP 2024, 1950–1976. https://doi.org/10.18653/v1/2024.findings-emnlp.108
Ziemski, M., Junczys-Dowmunt, M., & Pouliquen, B. (2016). The United Nations Parallel Corpus v1.0. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), Portorož, 3530–3534. https://aclanthology.org/L16-1561/
Zou, K., Khalifa, M., & Wang, L. (2025). On Many-Shot In-Context Learning for Long-Context Evaluation. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), 25605–25639. https://doi.org/10.18653/v1/2025.acl-long.1245
Zouhar, V., Bafna, N., Choudhary, M., Züfle, M., Rajaee, S., Chen, P., et al. (2026). Last Translation Benchmark. Preprint, arXiv:2609.04173. http://arxiv.org/abs/2609.04173
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs. https://www.hachettebookgroup.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/