Christopher Potts
CURRICULUM VITAE

November 8, 2025

Employment:Professor of Linguistics, and, by courtesy, of Computer Science, Stanford University
Address:Department of Linguistics
Margaret Jacks Hall, Bldg 460
Stanford University
Stanford   CA   94305-2150
E-mail:
Website:http://web.stanford.edu/~cgpotts/

Education

Academic employment

Other professional experience

Funded projects

Publications

  1. Kuditipudi, Rohith;* Jing Huang;* Sally Zhu;* Diyi Yang; Christopher Potts; and Percy Liang. 2025. Blackbox model provenance via palimpsestic membership inference. To appear in Proceedings of NeurIPS 2025.
  2. Csordás, Róbert; Christopher D. Manning; and Christopher Potts. 2025. Do language models use their depth efficiently? To appear in Proceedings of NeurIPS 2025.
  3. Wu, Zhengxuan;* Qinan Yu;* Aryaman Arora; Christopher D. Manning; and Christopher Potts. 2025. Improved representation steering for language models. To appear in Proceedings of NeurIPS 2025.
  4. Liang, Weixin; Yaohui Zhang; Zhengxuan Wu; Haley Lepp; Wenlong Ji; Xuandong Zhao; Hancheng Cao; Sheng Liu; Siyu He; Zhi Huang; Diyi Yang; Christopher Potts; Christopher D. Manning; and James Zou. 2025. Quantifying large language model usage in scientific papers. Nature Human Behavior.
  5. Geiger, Atticus; Duligur Ibeling; Amir Zur; Maheep Chaudhary; Sonakshi Chauhan; Jing Huang; Aryaman Arora; Zhengxuan Wu; Noah Goodman; Christopher Potts; and Thomas Icard. 2025. Causal abstraction: A theoretical foundation for mechanistic interpretability. Journal of Machine Learning Research 26(83): 1–64.
  6. Kallini, Julie; Dan Jurafsky; Christopher Potts; and Martijn Bartelds. 2025. False friends are not foes: Investigating vocabulary overlap in multilingual language models. In Findings of the Association for Computational Linguistics: EMNLP 2025, 21138–21154. Suzhou, China: Association for Computational Linguistics.
  7. Jabbar, Ahmad; Cleo Condoravdi; and Christopher Potts. 2025. Distinguishing fair from unfair compositional generalization tasks. In Findings of the Association for Computational Linguistics: EMNLP 2025, 20796–20807. Suzhou, China: Association for Computational Linguistics.
  8. Boguraev, Sasha; Christopher Potts; and Kyle Mahowald. 2025. Causal interventions reveal shared structure across English filler-gap constructions. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 25032–25053. Suzhou, China: Association for Computational Linguistics. [Outstanding Paper Award]
  9. West, Peter and Christopher Potts. 2025. Base models beat aligned models at randomness and creativity. In Proceedings of CoLM.
  10. Arora, Aryaman; Dan Jurafsky; Christopher Potts; and Noah D. Goodman. 2025. Bayesian scaling laws for in-context learning. In Proceedings of CoLM.
  11. Wu, Zhengxuan;* Aryaman Arora;* Atticus Geiger; Zheng Wang; Jing Huang; Dan Jurafsky; Christopher D. Manning; and Christopher Potts. 2025. AxBench: Steering LLMs? Even simple baselines outperform sparse autoencoders. In Forty-second International Conference on Machine Learning.
  12. Huang, Jing;* Junyi Tao;* Thomas Icard; Diyi Yang; and Christopher Potts. 2025.Internal causal mechanisms robustly predict language model out-of-distribution behaviors. In Forty-second International Conference on Machine Learning.
  13. Scheerer, Jan Luca; Matei Zaharia; Christopher Potts; Gustavo Alonso; and Omar Khattab. 2025. WARP: An efficient engine for multi-vector retrieval. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2504–2512. New York: Association for Computing Machinery. [Best Paper Award]
  14. Huang, Kaili;* Thejas Venkatesh;* Uma Dingankar;* Antonio Mallia; Daniel Campos; Jian Jiao; Christopher Potts; Matei Zaharia; Kwabena Boahen; Omar Khattab; Saarthak Sarup; and Keshav Santhanam. 2025. ColBERT-Serve: Efficient multi-stage memory-mapped scoring. In Advances in Information Retrieval: 47th European Conference on Information Retrieval, Part IV, 21–30. Springer-Verlag.
  15. Sun, Jiuding; Jing Huang; Sidharth Baskaran; Karel D'Oosterlinck; Christopher Potts; Michael Sklar; and Atticus Geiger. 2025. HyperDAS: Towards automating mechanistic interpretability with hypernetworks. In The Thirteenth International Conference on Learning Representations.
  16. Kallini, Julie; Shikhar Murty; Christopher D. Manning; Christopher Potts; and Róbert Csordás. 2025. MrT5: Dynamic token merging for efficient byte-level language models. In The Thirteenth International Conference on Learning Representations.
  17. Thrush, Tristan; Christopher Potts; and Tatsunori Hashimoto. 2025. Improving pretraining data using perplexity correlations. In The Thirteenth International Conference on Learning Representations.
  18. D'Oosterlinck, Karel; Winnie Xu; Chris Develder; Thomas Demeester; Amanpreet Singh; Christopher Potts; Douwe Kiela; and Shikib Mehri. 2025. Anchored Preference Optimization and Contrastive Revisions: Addressing underspecification in alignment. Transactions of the ACL 13: 442-460.
  19. Sun, Jiuding; Jing Huang; Sidharth Baskaran; Karel D'Oosterlinck; Christopher Potts; Michael Sklar; and Atticus Geiger. 2025. HyperDAS: Towards automating mechanistic interpretability with hypernetworks. In The Thirteenth International Conference on Learning Representations.
  20. Kallini, Julie; Shikhar Murty; Christopher D. Manning; Christopher Potts; and Róbert Csordás. 2025. MrT5: Dynamic token merging for efficient byte-level language models. In The Thirteenth International Conference on Learning Representations.
  21. Thrush, Tristan; Christopher Potts; and Tatsunori Hashimoto. 2025. Improving pretraining data using perplexity correlations. In The Thirteenth International Conference on Learning Representations.
  22. Nefdt, Ryan M. and Christopher Potts. 2024. Compositionality. In Michael C.'Frank and Asifa Majid, eds., Open Encyclopedia of Cognitive Science. MIT Press.
  23. Csordás, Róbert; Kazuki Irie; Jürgen Schmidhuber; Christopher Potts; Christopher D. Manning. 2024. MoEUT: Mixture-of-Experts Universal Transformers. In Advances in Neural Information Processing Systems 37: 28589–28614. Curran Associates, Inc.
  24. Wu, Zhengxuan;* Aryaman Arora;* Zheng Wang; Atticus Geiger; Dan Jurafsky; Christopher D. Manning; and Christopher Potts. 2024. ReFT: Representation finetuning for language models. In Advances in Neural Information Processing Systems 37: 63908-63962. Curran Associates, Inc. [
  25. Potts, Christopher. 2024. Characterizing English Preposing in PP constructions. Journal of Linguistics.
  26. Everaert, Dante; Rohit Patki; Tianqi Zheng; and Christopher Potts. 2024. AmazonQAC: A large-scale, naturalistic query autocomplete dataset. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 1046–1055. Miami, Florida: Association for Computational Linguistics.
  27. Guo, Xuan; Rohit Patki; Dante Everaert; and Christopher Potts. 2024. Retrieval augmented spelling correction for E-commerce applications. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 73–79. Miami, Florida: Association for Computational Linguistics.
  28. Csordás, Róbert; Christopher Potts; Christopher D. Manning; and Atticus Geiger. 2024. Recurrent neural networks learn to store and generate sequences using non-linear representations. In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 248–262. Miami, Florida: Association for Computational Linguistics.
  29. Soylu, Dilara; Christopher Potts; and Omar Khattab. 2024. Fine-tuning and prompt optimization: Two great steps that work better together. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 10696–10710. Miami, Florida: Association for Computational Linguistics.
  30. Opsahl-Ong, Krista; Michael J. Ryan; Josh Purtell; David Broman; Christopher Potts; Matei Zaharia; and Omar Khattab. 2024. Optimizing instructions and demonstrations for multi-stage language model programs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 9340–9366. Miami, Florida: Association for Computational Linguistics.
  31. Huang, Jing; Diyi Yang;* and Christopher Potts.* 2024. Demystifying verbatim memorization in large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 10711–10732. Miami, Florida: Association for Computational Linguistics.
  32. Naik, Nandita Shankar; Christopher Potts; and Elisa Kreiss. 2024. CommVQA: Situating visual question answering in communicative contexts. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 13362–13377. Miami, Florida: Association for Computational Linguistics.
  33. Zur, Amir; Elisa Kreiss; Karel D'Oosterlinck; Christopher Potts; and Atticus Geiger. 2024. Updating CLIP to prefer descriptions over captions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 20178–20187. Miami, Florida: Association for Computational Linguistics.
  34. Liu, Houjun; John Bauer; Karel D'Oosterlinck; Christopher Potts; and Christopher D. Manning. 2024. MSCAW-coref: Multilingual, singleton and conjunction-aware word-level coreference resolution. In Proceedings of The Seventh Workshop on Computational Models of Reference, Anaphora and Coreference, 33–40, Miami, Florida: Association for Computational Linguistics.
  35. Huang, Jing; Zhengxuan Wu; Christopher Potts; Mor Geva; and Atticus Geiger. 2024. RAVEL: Evaluating interpretability methods on disentangling language model representations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 8669–8687. Bangkok, Thailand: Association for Computational Linguistics.
  36. Arora, Aryaman; Dan Jurafsky; and Christopher Potts. 2024. CausalGym: Benchmarking causal interpretability methods on linguistic tasks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 14638–14663. Bangkok, Thailand: Association for Computational Linguistics, 14638–14663. Bangkok, Thailand: Association for Computational Linguistics. [Outstanding Paper Award]
  37. Kallini, Julie; Isabel Papadimitriou; Richard Futrell; Kyle Mahowald; and Christopher Potts. 2024. Mission: Impossible Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 14691-14714. Bangkok, Thailand: Association for Computational Linguistics, 14691–14714. Bangkok, Thailand: Association for Computational Linguistics. [Best Paper Award]
  38. Thrush, Tristan; Jared Moore; Miguel Monares; Christopher Potts; and Douwe Kiela. 2024. I am a Strange Dataset: Metalinguistic tests for language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 8888–8907. Bangkok, Thailand: Association for Computational Linguistics, 8888–8907. Bangkok, Thailand: Association for Computational Linguistics.
  39. Verma, Arnav; Kushin Mukherjee; Christopher Potts; Elisa Kreiss; and Judith E. Fan. 2024. Evaluating human and machine understanding of data visualizations. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.
  40. Liang, Weixin;* Yaohui Zhang;* Zhengxuan Wu;* Haley Lepp; Wenlong Ji; Xuandong Zhao; Hancheng Cao; Sheng Liu; Siyu He; Zhi Huang; Diyi Yang; Christopher Potts; Christopher D. Manning; and James Y. Zou. 2024. Mapping the increasing use of LLMs in scientific papers. First Conference on Language Modeling.
  41. Budur, Emrah; Rıza Özçelik; Dilara Soylu; Omar Khattab; Tunga Güngör; and Christopher Potts. 2024. Building efficient and effective OpenQA systems for low-resource languages. Knowledge-Based Systems 203: 112243.
  42. Wu, Zhengxuan; Atticus Geiger; Aryaman Arora; Jing Huang; Zheng Wang; Noah D. Goodman; Christopher D. Manning; and Christopher Potts. 2024. pyvene: A library for understanding and improving PyTorch models via interventions. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), 158–165. Mexico City: Association for Computational Linguistics.
  43. Saad-Falcon, Jon; Omar Khattab; Christopher Potts; and Matei Zaharia. 2024. ARES: An automated evaluation framework for retrieval-augmented generation systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 338–354. Mexico City: Association for Computational Linguistics.
  44. Khattab, Omar; Arnav Singhvi; Paridhi Maheshwari; Zhiyuan Zhang; Keshav Santhanam; Sri Vardhamanan; Saiful Haq; Ashutosh Sharma; Thomas T. Joshi; Hanna Moazam; Heather Miller; Matei Zaharia; and Christopher Potts. 2024. DSPy: Compiling declarative language model calls into self-improving pipelines. In Proceedings of The Twelfth International Conference on Learning Representations.
  45. Kreiss, Elisa; Eric Zelikman; Christopher Potts; and Nick Haber. 2024. ContextRef: Evaluating referenceless metrics for image description generation. In Proceedings of The Twelfth International Conference on Learning Representations.
  46. Everaert, Dante and Christopher Potts. 2024. GIO: Gradient Information Optimization for training dataset selection. In Proceedings of The Twelfth International Conference on Learning Representations.
  47. D'Oosterlinck, Karel; François Remy; Johannes Deleu; Thomas Demeester; Chris Develder; Klim Zaporojets; Aneiss Ghodsi; Simon Ellershaw; Jack Collins; and Christopher Potts. 2023. BioDEX: Large-scale biomedical adverse drug event extraction for real-world pharmacovigilance. In Findings of the Association for Computational Linguistics: EMNLP 2023, 13425–13454. Singapore: Association for Computational Linguistics.
  48. Zhong, Zexuan; Zhengxuan Wu; Christopher D. Manning; Christopher Potts; and Danqi Chen. 2023. MQuAKE: Assessing knowledge editing in language models via multi-hop questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 15686–15702. Singapore: Association for Computational Linguistics.
  49. Wu, Zhengxuan; Atticus Geiger; Christopher Potts; and Noah D. Goodman. 2023. Interpretability at scale: Identifying causal mechanisms in Alpaca. In Advances in Neural Information Processing Systems, 78205-78226. Curran Associates, Inc.
  50. Wu, Zhengxuan; Christopher D. Manning; and Christopher Potts. 2023. ReCOGS: How Incidental details of a logical form overshadow an evaluation of semantic interpretation. Transactions of the Association for Computational Linguistics 11: 1719–1733.
  51. Geiger, Atticus; Zhengxuan Wu; Christopher Potts; Thomas Icard; Noah D. Goodman. 2023. Finding alignments between interpretable causal variables and distributed neural representations. In Proceedings of Causal Learning and Reasoning 2024.
  52. Saad-Falcon, Jon; Omar Khattab; Keshav Santhanam; Radu Florian; Martin Franz; Salim Roukos; Avirup Sil; Md Arafat Sultan; and Christopher Potts. 2023. UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 11265–11279. Singapore: Association for Computational Linguistics.
  53. D'Oosterlinck, Karel; Thomas Demeester; Chris Develder; and Christopher Potts. 2023. Flexible model interpretability through natural language model editing. In BlackBoxNLP 2023. Singapore: Association for Computational Linguistics.
  54. D'Oosterlinck, Karel; Semere Kiros Bitew; Brandon Papineau; Christopher Potts; Thomas Demeester; Chris Develder. 2023. CAW-coref: Conjunction-Aware Word-level coreference resolution. In Proceeedings of the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference, 8–14. Singapore: Association for Computational Linguistics.
  55. Zhang, Jingfen; Xuan Guo; Sravan Bodapati; and Christopher Potts. 2023. Multi-teacher distillation for multilingual spelling correction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, 142–151. Singapore: Association for Computational Linguistics.
  56. Huang, Jing; Atticus Geiger; Karel D'Oosterlinck; Zhengxuan Wu; and Christopher Potts. 2023. Rigorously assessing natural language explanations of neurons. In Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 317–331. Singapore: Association for Computational Linguistics.
  57. Naik, Nandita; Christopher Potts; Elisa Kreiss. 2023. Context-VQA: Towards context-aware and purposeful Visual Question Answering. ICCV: 5th Workshop on Closing the Loop Between Vision and Language.
  58. She, Jingyuan Selena; Christopher Potts; Samuel R. Bowman; Atticus Geiger. 2023. ScoNe: Benchmarking negation reasoning in language models with fine-tuning and in-context learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1803–1821. Toronto: Association for Computational Linguistics.
  59. Kreiss, Elisa; Krishna Srinivasan; Tiziano Piccardi; Jesus Adolfo Hermosillo; Cynthia Bennett; Michael S. Bernstein; Meredith Ringel Morris; and Christopher Potts. 2023. Characterizing image accessibility on Wikipedia across languages. Wiki Workshop 2023.
  60. Huang, Jing; Zhengxuan Wu; Kyle Mahowald; and Christopher Potts. 2023. Inducing character-level structure in subword-based language models with Type-level Interchange Intervention Training. In Findings of the Association for Computational Linguistics: ACL 2023, 12163–12180. Toronto: Association for Computational Linguistics.
  61. Sosa, Daniel N.; Malavika Suresh; Christopher Potts; and Russ B. Altman. 2023. Detecting contradictory COVID-19 drug efficacy claims from biomedical literature. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1803–1821. Toronto: Association for Computational Linguistics.
  62. Santhanam, Keshav;* Jon Saad-Falcon;* Martin Franz; Omar Khattab; Avirup Sil; Radu Florian; Md Arafat Sultan; Salim Roukos; Matei Zaharia; and Christopher Potts. 2023. Moving beyond downstream task accuracy for information retrieval benchmarking. In Findings of the Association for Computational Linguistics: ACL 2023, 11613–11628. Toronto: Association for Computational Linguistics.
  63. Petersen, Erika and Christopher Potts. 2023. Lexical semantics with Large Language Models: A case study of English break. In Findings of the Association for Computational Linguistics: EACL 2023, 490–511. Dubrovnik: Association for Computational Linguistics.
  64. Wu, Zhengxuan;* Karel D'Oosterlinck;* Atticus Geiger;* Amir Zur; and Christopher Potts. 2023. Causal Proxy Models for concept-based model explanations. In Proceedings of ICML.
  65. Kreiss, Elisa; Cynthia Bennett; Shayan Hooshmand; Eric Zelikman; Meredith Ringel Morris; and Christopher Potts. 2022. Context matters for image descriptions for accessibility: Challenges for referenceless evaluation metrics. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 4685–4697. Association for Computational Linguistics
  66. Kreiss, Elisa; Fei Fang; Noah D. Goodman; and Christopher Potts. 2022. Concadia: Towards image-based text generation with a purpose. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 4667–4684. Association for Computational Linguistics.
  67. Li, Siyan; Riley Carlson; and Christopher Potts. 2022. Systematicity in GPT-3's interpretation of novel English noun compounds. In Findings of EMNLP, 717–728. Association for Computational Linguistics.
  68. Abraham, Eldar David;* Karel D'Oosterlinck;* Amir Feder;* Yair Ori Gat;* Atticus Geiger;* Christopher Potts;* Roi Reichart;* and Zhengxuan Wu.* 2022. CEBaB: Estimating the causal effects of real-world concepts on NLP model behavior. In Proceedings of NeurIPS, 17582–17596.
  69. Santhanam, Keshav;* Omar Khattab;* Christopher Potts; and Matei Zaharia. 2022. PLAID: An efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 1747–1756. Association for Computing Machinery.
  70. Fang, Fei; Kunal Sinha; Noah D. Goodman; Christopher Potts; and Elisa Kreiss. 2022. Color overmodification emerges from data-driven learning and pragmatic reasoning. In Proceedings of the Cognitive Science Society.
  71. Santhanam, Keshav;* Omar Khattab;* Jon Saad-Falcon; Christopher Potts; and Matei Zaharia. 2022. ColBERTv2: Effective and efficient retrieval via lightweight late interaction. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 3715–3734. Association for Computational Linguistics.
  72. Wu, Zhengxuan;* Atticus Geiger;* Josh Rozner; Elisa Kreiss; Hanson Lu; Thomas Icard; Christopher Potts; and Noah D. Goodman. 2022. Causal distillation for language models. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4288–4295. Association for Computational Linguistics.
  73. Geiger, Atticus;* Zhengxuan Wu;* Hanson Lu;* Josh Rozner; Elisa Kreiss; Thomas Icard; Noah D. Goodman; and Christopher Potts. 2022. Inducing causal structure for interpretable neural networks. In Proceeding of ICML.
  74. Paranjape, Ashwin; Omar Khattab; Christopher Potts; Matei Zaharia; and Christopher D. Manning. 2021. Hindsight: Posterior-guided training of retrievers for improved open-ended generation. arXiv:2110.07752. In Proceedings of ICLR.
  75. Geiger, Atticus; Alexandra Carstensen; Michael C. Frank; and Christopher Potts. 2022. Relational reasoning and generalization using non-symbolic neural networks. Psychological Review.
  76. Wu, Zhengxuan; Nelson F. Liu; and Christopher Potts. 2021. Identifying the limits of cross-domain knowledge transfer for pretrained models. In Proceedings of RepL4NLP. [Best Paper Award]
  77. Beam, Elizabeth; Christopher Potts; Russell A. Poldrack; and Amit Etkin. 2022. A data-driven framework for mapping domains of human neurobiology. Nature Neuroscience.
  78. Geiger, Atticus; Hanson Lu; Thomas Icard; and Christopher Potts. 2021. Causal abstractions of neural networks. In Proceedings of NeurIPS, 9574-9586.
  79. Khattab, Omar; Christopher Potts; and Matei Zaharia. 2021. Baleen: Robust multi-hop reasoning at scale via condensed retrieval. In Proceedings of NeurIPS, 27670-27682.
  80. Ma, Zhiyi; Kawin Ethayarajh; Tristan Thrush; Somya Jain; Ledell Wu; Robin Jia; Christopher Potts; Adina Williams; and Douwe Kiela. 2021. Dynaboard: An evaluation-as-a-service platform for holistic next-generation benchmarking. In Proceedings of NeurIPS, 10351-10367.
  81. Rozner, Josh; Christopher Potts; and Kyle Mahowald. 2021. Decrypting cryptic crosswords: Semantically complex wordplay puzzles as a target for NLP. In Proceedings of NeurIPS, 11409-11421.
  82. Wu, Zhengxuan;* Elisa Kreiss;* Desmond C. Ong; and Christopher Potts. 2021. ReaSCAN: Compositional reasoning in language grounding. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks.
  83. West, Robert; Jure Leskovec; and Christopher Potts. 2021. Post-mortem memory of public figures in news and social media. Proceedings of the National Academy of Sciences 118(38): e2106152118.
  84. Khattab, Omar; Christopher Potts; and Matei Zaharia. 2021. Relevance-guided Supervision for OpenQA with ColBERT. Transactions of the ACL 9: 929–944.
  85. Kiela, Douwe; Max Bartolo; Yixin Nie; Divyansh Kaushik; Atticus Geiger; Zhengxuan Wu; Bertie Vidgen; Grusha Prasad; Amanpreet Singh; Pratik Ringshia; Zhiyi Ma; Tristan Thrush; Sebastian Riedel; Zeerak Waseem; Pontus Stenetorp; Robin Jia; Mohit Bansal; Christopher Potts; and Adina Williams. 2021. Dynabench: Rethinking benchmarking in NLP. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4110-4124. Association for Computational Linguistics.
  86. Potts, Christopher; Zhengxuan Wu; Atticus Geiger; and Douwe Kiela. 2021. DynaSent: A dynamic benchmark for sentiment analysis. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2388–2404. Association for Computational Linguistics.
  87. Geiger, Atticus; Kyle Richardson; and Christopher Potts. 2020. Neural natural language inference models partially embed theories of lexical entailment and negation. In Proceedings of BlackBoxNLP 2020, 163–173. Association for Computational Linguistics.
  88. Esposito, Lewis and Christopher Potts. 2020. A probabilistic pragmatics for English singular some. Proceedings of Semantics and Linguistic Theory 30. Ithaca, NY: Cornell University.
  89. Kreiss, Elisa; Zijian Wang; and Christopher Potts. 2020. Modeling subjective assessments of guilt in newspaper crime narratives. In Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL), 56–68. Association for Computational Linguistics.
  90. Budur, Emrah; Rıza Özçelik; Tunga Güngör; and Christopher Potts. 2020. Data and Representation for Turkish Natural Language Inference. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 8253–8267. Association for Computational Linguistics.
  91. Nie, Allen; Reuben Cohn-Gordon; and Christopher Potts. 2020. Pragmatic Issue-Sensitive Image Captioning. In Findings of the Association for Computational Linguistics: EMNLP 2020, 1924–1938. Association for Computational Linguistics.
  92. Newman, Benjamin; Reuben Cohn-Gordon; and Christopher Potts. 2010. Communication-based evaluation for natural language generation. In Proceedings of the Society for Computation in Linguistics. New Orleans: Linguistic Society of America.
  93. Geiger, Atticus; Ignacio Cases; Lauri Karttunen; and Christopher Potts. 2019. Posing fair generalization tasks for Natural Language Inference. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 4475–4485. Hong Kong: Association for Computational Linguistics.
  94. Wang, Zijian and Christopher Potts. 2019. TalkDown: A corpus for condescension detection in context. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 3702–3710. Hong Kong: Association for Computational Linguistics.
  95. Cases, Ignacio; Clemens Rosenbaum; Matthew Riemer; Atticus Geiger; Tim Klinger; Alex Tamkin; Olivia Li; Sandhini Agarwal; Joshua D. Greene; Dan Jurafsky; Christopher Potts; and Lauri Karttunen. 2019. Recursive routing networks: Learning to compose modules for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 3631–3648. Minneapolis: Association for Computational Linguistics.
  96. Tao, Yifeng; Bruno Godefroy; Guillaume Genthial; and Christopher Potts. 2019. Effective feature representation for clinical text concept extraction. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, 1–14. Minneapolis: Association for Computational Linguistics.
  97. Potts, Christopher. 2019. A case for deep learning in semantics: Response to Pater. Language 95(1): e115–e125.
  98. Cohn-Gordon, Reuben; Noah D. Goodman; and Christopher Potts. 2019. An incremental iterated response model of pragmatics. In Proceedings of the Society for Computation in Linguistics, 81-90. New York: Linguistic Society of America.
  99. Kolchinski, Y. Alex and Christopher Potts. 2018. Representing social media users for sarcasm detection. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics.
  100. Lengerich, Benjamin J.; Andrew L. Maas; and Christopher Potts. 2018. Retrofitting distributional embeddings to knowledge graphs with functional relations. Proceedings of the 27th International Conference on Computational Linguistics, 2423–2436. Santa Fe: Association for Computational Linguistics.
  101. Cohn-Gordon, Reuben; Noah D. Goodman; and Christopher Potts. 2018. Pragmatically informative image captioning with character-level inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 439–443. New Orleans: Association for Computational Linguistics.
  102. Dingwall, Nicholas and Christopher Potts. 2018. Mittens: An extension of GloVe for learning domain-specialized representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 212–217. New Orleans: Association for Computational Linguistics.
  103. Monroe, Will; Jennifer Hu; Andrew Jong; and Christopher Potts. 2018. Generating bilingual pragmatic color references. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2155–2165. New Orleans: Association for Computational Linguistics.
  104. Monroe, Will; Robert X.D. Hawkins; Noah D. Goodman; and Christopher Potts. 2017. Colors in context: a pragmatic neural model for grounded language understanding. Transactions of the Association for Computational Linguistics 5: 325–338.
  105. Cases, Ignacio; Minh-Thang Luong; and Christopher Potts. 2017. On the effective use of pretraining for natural language inference. Ms., Stanford University.
  106. de Marneffe, Marie-Catherine and Christopher Potts. 2017. Developing linguistic theories using annotated corpora. In Nancy Ide and James Pustejovsky, eds., The Handbook of Linguistic Annotation, 411–438. Berlin: Springer.
  107. Srivastava, Sameer B.; Amir Goldberg; V. Govind Manian; and Christopher Potts. 2016. Enculturation trajectories: language, cultural adaptation, and individual outcomes in organizations. Management Science.
  108. Goldberg, Amir; Sameer B. Srivastava; V. Govind Manian; Will Monroe; and Christopher Potts. 2016. Fitting in or standing out? The tradeoffs of structural and cultural embeddedness. American Sociological Review 81(6): 1190–1222.
  109. Potts, Christopher; Daniel Lassiter; Roger Levy; and Michael C. Frank. 2016. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. Journal of Semantics 33(4): 755–802.
  110. Monroe, Will; Noah D. Goodman; and Christopher Potts. 2016. Learning to generate compositional color descriptions. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2243–2248. Austin, TX: Association for Computational Linguistics.
  111. Bowman, Samuel R.; Jon Gauthier; Abhinav Rastogi; Raghav Gupta; Christopher D. Manning; and Christopher Potts. 2016. A fast unified model for parsing and sentence understanding. Proceedings of 54th Annual Meeting of the Association for Computational Linguistics, 1466–1477. Association for Computational Linguistics.
  112. Jeong, Sunwoo and Christopher Potts. 2016. Intonational sentence-type conventions for perlocutionary effects: An experimental investigation. In Mary Moroney, Carol-Rose Little, Dan Burgdorf, and Jacob Collard, eds., Proceedings of Semantics and Linguistic Theory 26, 1–22. Ithaca, NY: CLC Publications
  113. Bowman, Samuel R.; Christopher D. Manning; and Christopher Potts; 2015. Tree-structured composition in neural networks without tree-structured architectures. In Proceedings of the NIPS 2015 Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches. Montreal.
  114. de Marneffe, Marie-Catherine; Marta Recasens; and Christopher Potts. 2015. Modeling the lifespan of discourse entities with application to coreference resolution. Journal of Artificial Intelligence Research 52: 445–475.
  115. Monroe, Will and Christopher Potts. 2015. Learning in the Rational Speech Acts model. In Proceedings of the 20th Amsterdam Colloquium. Amsterdam: ILLC.
  116. Bowman, Samuel R.; Christopher Potts; and Christopher D. Manning. 2015. Recursive neural networks can learn logical semantics. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. Beijing: Association for Computational Linguistics.
  117. Bowman, Samuel R.; Gabor Angeli; Christopher Potts; and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 632–642. Association for Computational Linguistics. [Best New Data Set or Resource Award]
  118. Chang, Angel; Will Monroe; Manolis Savva; Christopher Potts; and Christopher D. Manning. 2015. Text to 3d scene generation with rich lexical grounding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference of the Asian Federation of Natural Language Processing, 53–62 Association for Computational Linguistics.
  119. Potts, Christopher. 2015. Presupposition and implicature. In Shalom Lappin and Chris Fox, eds., The Handbook of Contemporary Semantic Theory, 2nd edn., 168–202. Oxford: Wiley-Blackwell.
  120. Potts, Christopher and Roger Levy. 2015. Negotiating lexical uncertainty and speaker expertise with disjunction. In Proceedings of the 41st Annual Meeting of the Berkeley Linguistics Society.
  121. Liang, Percy and Christopher Potts. 2015. Bringing machine learning and compositional semantics together. Annual Review of Linguistics 1(1): 355–376.
  122. Bowman, Samuel R.; Christopher Potts; and Christopher D. Manning. 2015. Learning distributed word representations for natural logic reasoning. In Proceedings of Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, 10–13. Association for the Advancement of Artificial Intelligence.
  123. West, Robert; Hristo S. Paskov; Jure Leskovec; and Christopher Potts. 2014. Exploiting social network structure for person-to-person sentiment analysis. Transactions of the Association for Computational Linguistics 2(2): 297–310.
  124. Vogel, Adam; Andrés Gómez Emilsson; Michael C. Frank; Dan Jurafsky; and Christopher Potts. 2014. Learning to reason pragmatically with cognitive limitations. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 3055–3060. Quebec City: Cognitive Science Society.
  125. Sudhof, Moritz; Andrés Gómez Emilsson; Andrew L. Maas; and Christopher Potts. 2014. Sentiment expression conditioned by affective transitions and social forces. In Proceedings of 20th Conference on Knowledge Discovery and Data Mining, 1136–1145. New York.
  126. Socher, Richard; Alex Perelygin; Jean Wu; Jason Chuang; Christopher D. Manning, Andrew Y. Ng; and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1631–1642. Association for Computational Linguistics.
  127. Vogel, Adam; Dan Jurafsky; and Christopher Potts. 2013. Implicatures and nested beliefs in approximate decentralized-POMDPs. Proceedings of the 2013 Annual Conference of the Association for Computational Linguistics, 74–80. Association for Computational Linguistics.
  128. Acton, Eric K. and Christopher Potts. 2013. That straight talk: Sarah Palin and the sociolinguistics of demonstratives. Journal of Sociolinguistics 18(1): 3–31.
  129. Danescu-Niculescu-Mizil, Cristian; Moritz Sudhof; Dan Jurafsky; Jure Leskovec; and Christopher Potts. 2013. A computational approach to politeness with application to social factors. Proceedings of the 2013 Annual Conference of the Association for Computational Linguistics, 250–259. Association for Computational Linguistics. [Best Paper Award nomination]
  130. Vogel, Adam; Max Bodoia; Dan Jurafsky; and Christopher Potts. 2013. Emergence of Gricean maxims from multi-agent decision theory. Human Language Technologies: The 2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 1072–1081. Association for Computational Linguistics.
  131. Recasens, Marta; Marie-Catherine de Marneffe; and Christopher Potts. 2013. The life and death of discourse entities: identifying singleton mentions. In Human Language Technologies: The 2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 627–633. Association for Computational Linguistics. [Best Short Paper Award]
  132. Danescu-Niculescu-Mizil, Cristian; Robert West; Dan Jurafsky; Jure Leskovec; and Christopher Potts. 2013. No country for old members: user lifecycle and linguistic change in online communities. Proceedings of the 22nd World Wide Web Conference, 307–317. ACM. [Best Paper Award]
  133. de Marneffe, Marie-Catherine; Christopher D. Manning; and Christopher Potts. 2012. Did it happen? The pragmatic complexity of veridicality assessment. Computational Linguistics 38(2): 301–333.
  134. Potts, Christopher. 2012. Goal-driven answers in the Cards dialogue corpus. In Nathan Arnett and Ryan Bennett, eds., Proceedings of the 30th West Coast Conference on Formal Linguistics, 1–20. Somerville, MA: Cascadilla Press.
  135. Overgoor, Jan; Ellery Wulczyn; and Christopher Potts. 2012. Trust propagation with mixed-effects models. Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence.
  136. Djalali, Alex; Sven Lauer; and Christopher Potts. 2012. Corpus evidence for preference-driven interpretation. In Maria Aloni, Vadim Kimmelman, Floris Roelofsen, Galit Weidman Sassoon, Katrin Schulz, and Matthijs Westera, eds., Proceedings of the 18th Amsterdam Colloquium: Revised Selected Papers. Berlin: Springer.
  137. de Marneffe, Marie-Catherine; Christopher D. Manning; and Christopher Potts. 2011. Veridicality and utterance meaning. Proceedings of the Fifth IEEE International Conference on Semantic Computing: Workshop on Semantic Annotation for Computational Linguistic Resources, 430–437. IEEE Computer Society Press.
  138. Maas, Andrew L.; Raymond E. Daly; Peter T. Pham; Dan Huang; Andrew Y. Ng; and Christopher Potts. 2011. Learning word vectors for sentiment analysis. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics.
  139. Miller, Mahalia; Conal Sathi; Daniel Wiesenthal; Jure Leskovec; and Christopher Potts. 2011. Sentiment flow through hyperlink networks. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence.
  140. Djalali, Alex; David Clausen; Sven Lauer; Karl Schultz; and Christopher Potts. 2011. Modeling expert effects and common ground using Questions Under Discussion. Proceedings of the AAAI Workshop on Building Representations of Common Ground with Intelligent Agents. Association for the Advancement of Artificial Intelligence.
  141. Potts, Christopher. 2011. Teaching pragmatics. In Koenraad Kuiper, ed., Teaching Linguistics, 51–65. London: Equinox Publishing.
  142. Potts, Christopher. 2011. Pragmatics. In Ruslan Mitkov, ed., The Oxford Handbook of Computational Linguistics, 2nd edition. Oxford University Press.
  143. de Marneffe, Marie-Catherine; Christopher D. Manning; and Christopher Potts. 2010. ''Was it good? It was provocative.'' Learning the meaning of scalar adjectives. In Proceedings 48th Annual Meeting of the Association for Computational Linguistics, 167–176. Association for Computational Linguistics.
  144. Potts, Christopher. 2011. On the negativity of negation. In Nan Li and David Lutz, eds., Proceedings of Semantics and Linguistic Theory 20, 636–659. Ithaca, NY: CLC Publications.
  145. Munro; Robert; Steven Bethard; Victor Kuperman; Vicky Tzuyin Lai; Robin Melnick; Christopher Potts; Tyler Schnoebelen; and Harry Tily. 2010. Crowdsourcing and language studies: The new generation of linguistic data. In Proceedings of the NAACL 2010 Workshop on Creating Speech and Language Data With Amazon's Mechanical Turk, 122–130. Association for Computational Linguistics.
  146. Davis, Christopher and Christopher Potts. 2010. Affective demonstratives and the division of pragmatic labor. In Maria Aloni, Harald Bastiaanse, Tikitu de Jager, and Katrin Schulz, eds., Logic, Language, and Meaning: 17th Amsterdam Colloquium Revised Selected Papers, 42–52. Berlin: Springer.
  147. Potts, Christopher and Florian Schwarz. 2010. Affective 'this'. Linguistic Issues in Language Technology 3(5):1–30.
  148. Potts, Christopher; Joe Pater; Karen Jesney; Rajesh Bhatt; and Michael Becker. 2010. Harmonic Grammar with Linear Programming: From linear systems to linguistic typology. Phonology 27(1):1–41.
  149. Potts, Christopher. 2010. Semantics–pragmatics interactions. In Patrick Colm Hogan, ed., The Cambridge Encyclopedia of the Language Sciences, 758-759. Cambridge University Press.
  150. de Marneffe, Marie-Catherine; Scott Grimm; and Christopher Potts. 2009. Not a simple yes or no: Uncertainty in indirect answers. Proceedings of the 10th Annual SIGDIAL Meeting on Discourse and Dialogue, 136–143. Queen Mary University of London: Association for Computational Linguistics.
  151. Harris, Jesse A. and Christopher Potts. 2009. Predicting perspectival orientation for appositives. In Ryan Bochnak, Nassira Nicola, Peet Klecha, Jasmin Urban, Alice Lemieux, and Christina Weaver, eds., Proceedings of the 45th Annual Chicago Linguistic Society Meeting: The Main Session, 207–221. Chicago Linguistic Society.
  152. Harris, Jesse A. and Christopher Potts. 2009. Perspective-shifting with appositives and expressives. Linguistics and Philosophy 32(6):523–552.
  153. Potts, Christopher. 2009. Formal pragmatics. In Louise Cummings, ed., The Routledge Encyclopedia of Pragmatics, 167–170. London: Routledge.
  154. Constant, Noah; Christopher Davis; Christopher Potts; and Florian Schwarz. 2009. The pragmatics of expressive content: Evidence from large corpora. Sprache und Datenverarbeitung 33(1-2):5–21.
  155. Potts, Christopher; Luis Alonso-Ovalle; Ash Asudeh; Rajesh Bhatt Seth Cable; Christopher Davis; Yurie Hara; Angelika Kratzer; Eric McCready; Tom Roeper; and Martin Walkow. 2008. Expressives and identity conditions. Linguistic Inquiry 40(2):356–366.
  156. Potts, Christopher. 2008. Review Article: Hagit Borer's Structuring Sense, Vol I–II. Language 84(2):343–369.
  157. Potts, Christopher. 2008. The pragmatics of conventional implicature and expressive content. In Claudia Maienborn and Paul Portner, eds., Semantics: An International Handbook of Natural Language Meaning, Volume 3, 2516–2536. Berlin: Mouton de Gruyter.
  158. Potts, Christopher. 2007. The dimensions of quotation. In Chris Barker and Pauline Jacobson, eds., Direct Compositionality, 405–431. Oxford University Press.
  159. Davis, Christopher; Christopher Potts; and Margaret Speas. 2007. The pragmatic values of evidential sentences. In Masayuki Gibson and Tova Friedman, eds., Proceedings of Semantics and Linguistic Theory 17, 71–88. Ithaca, NY: CLC Publications.
  160. Potts, Christopher. 2007. The expressive dimension. Theoretical Linguistics 33(2):165–197.
  161. Potts, Christopher. 2007. The centrality of expressive indices: Reply to the commentaries. Theoretical Linguistics 33(2):255–268.
  162. Potts, Christopher. 2007. Into the conventional-implicature dimension. Philosophy Compass 4(2):665–679.
  163. Potts, Christopher. 2007. Conventional implicatures, a distinguished class of meanings. In Gillian Ramchand and Charles Reiss, eds., The Oxford Handbook of Linguistic Interfaces, 475–501. Oxford University Press.
  164. Potts, Christopher and Tom Roeper. 2006. The narrowing acquisition path: From expressive small clauses to declaratives. In Ljiljana Progovac, Kate Paesani, Eugenia Casielles, Ellen Barton, eds., The Syntax of Nonsententials: Multi-Disciplinary Perspectives, 183–201. Amsterdam: John Benjamins.
  165. Potts, Christopher. 2006. Conversational implicatures via general pragmatic pressures. In Takashi Washio, Akito Sakurai, Katsuto Nakajima, Hideaki Takeda, Satoshi Tojo, and Makoto Yokoo, eds., Japanese Society for Artificial Intelligence 2006, 205–218. Berlin: Springer.
  166. Potts, Christopher. 2006. Review of Siobhan Chapman, Paul Grice: Philosopher and Linguist. Mind 115:743–747.
  167. Potts, Christopher. 2006. How far can pragmatic mechanisms take us? Theoretical Linguistics 32(3):307–320.
  168. Potts, Christopher. 2005. Lexicalized intonational meaning. In Shigeto Kawahara, ed., University of Massachusetts Occasional Papers 30 (UMOP 30), 129–146. Amherst, MA: GLSA.
  169. Potts, Christopher. 2005. The Logic of Conventional Implicatures. Oxford Studies in Theoretical Linguistics. Oxford University Press.
  170. Potts, Christopher and Shigeto Kawahara. 2004. Japanese honorifics as emotive definite descriptions. In Kazuha Watanabe and Robert B. Young, eds., Proceedings of Semantics and Linguistic Theory 14, 235–254. CLC Publications: Ithaca, NY.
  171. Potts, Christopher. 2003. Expressive content as conventional implicature. In Makoto Kadowaki and Shigeto Kawahara (eds.), Proceedings of the North East Linguistic Society 33, 303–322. Amherst, MA: GLSA.
  172. 2003. The Logic of Conventional Implicatures. Department of Linguistics, University of California, Santa Cruz. Primary advisor: Geoffrey K. Pullum. Committee members: William A. Ladusaw and James McCloskey.
  173. Potts, Christopher and Geoffrey K. Pullum. 2002. Model theory and the content of OT constraints. Phonology 19(3):361–393.
  174. Potts, Christopher. 2002. The syntax and semantics of As-parentheticals. Natural Language and Linguistic Theory 20(3):623–689.
  175. Potts, Christopher. 2002. The lexical semantics of parenthetical-as and appositive-which. Syntax 5(1):55–88.
  176. Mikkelsen, Line and Christopher Potts, eds. 2002. Proceedings of the West Coast Conference on Formal Linguistics 21. Somerville, MA: Cascadilla Press.
  177. Potts, Christopher. 2002. NELS 33 conference report. Glot International.
  178. Potts, Christopher. 2002. WCCFL 21 conference report. Glot International 5:5.
  179. Potts, Christopher. 2001. No vacuous quantification constraints in syntax. Proceedings of the North East Linguistic Society 32, 451–470. Amherst, MA: GLSA.
  180. Potts, Christopher. 2001. Three types of transderivational constraint. In Séamas Mac Bhloscaidh (ed.), Syntax at Santa Cruz, Volume 3, 21–40. Linguistics Department, UC Santa Cruz.
  181. Potts, Christopher. 2000. (Only) some weak crossover effects repaired. Snippets 1:3.
  182. Potts, Christopher. 2000. When even no's neg is splitsville. Jorge Hankamer's Web Fest:
    http://ling.ucsc.edu//Jorge/index.html.

Manuscripts and technical reports

  1. Grant, Satchel; Simon Jerome Han; Alexa Tartaglini; and Christopher Potts. 2025. Addressing divergent representations from causal interventions on neural networks. arXiv:2511.04638.
  2. Tartaglini, Alexa R.; Satchel Grant; Daniel Wurgaft; Christopher Potts; and Judith E. Fan. 2025. Diagnosing bottlenecks in data visualization understanding by vision-language models. arXiv:2510.21740.
  3. Ziems, Noah;* Dilara Soylu;* Lakshya A. Agrawal;* Isaac Miller; Liheng Lai; Chen Qian; Kaiqiang Song; Meng Jiang; Dan Klein; Matei Zaharia; Karel D'Oosterlinck; Christopher Potts; and Omar Khattab. 2025. Multi-module GRPO: Composing policy gradients and prompt optimization for language model programs. arXiv:2508.04660.
  4. Agrawal, Lakshya A.; Shangyin Tan; Dilara Soylu; Noah Ziems; Rishi Khare; Krista Opsahl-Ong; Arnav Singhvi; Herumb Shandilya; Michael J. Ryan; Meng Jiang; Christopher Potts; Koushik Sen; Alexandros G. Dimakis; Ion Stoica; Dan Klein; Matei Zaharia; and Omar Khattab. 2025. GEPA: Reflective prompt evolution can outperform reinforcement learning. arXiv:2507.19457.
  5. Sun, Jiuding;* Sidharth Baskaran;* Zhengxuan Wu; Michael Sklar; Christopher Potts; and Atticus Geiger. 2025. HyperSteer: Activation steering at scale with hypernetworks. arXiv:2506.03292.
  6. Arora, Aryaman; Neil Rathi; Nikil Roashan Selvam; Róbert Csórdas; Dan Jurafsky; and Christopher Potts. 2025. Mechanistic evaluation of Transformers and state space models. arXiv:2505.13898.
  7. Xian, Jasper; Saron Samuel; Faraz Khoubsirat; Ronak Pradeep; Md Arafat Sultan; Radu Florian; Salim Roukos; Avirup Sil; Christopher Potts; and Omar Khattab. 2024. Prompts as auto-optimized training hyperparameters: Training best-in-class IR models from scratch with 10 gold labels. arXiv:2406.11706.
  8. D'Oosterlinck, Karel; Omar Khattab; François Remy; Thomas Demeester; Chris Develder; and Christopher Potts. 2024. In-context learning for extreme multi-label classification. arXiv:2401.12178.
  9. Wu, Zhengxuan; Atticus Geiger; Jing Huang; Aryaman Arora; Thomas Icard; Christopher Potts; and Noah D. Goodman. 2024. A reply to Makelov et al. (2023)'s ''Interpretability Illusion'' arguments.
  10. Zaharia, Matei; Omar Khattab; Lingjiao Chen; Jared Quincy Davis; Heather Miller; Christopher Potts; James Zou; Michael Carbin; Jonathan Frankle; Naveen Rao; and Ali Ghodsi. 2024. The shift from models to Compound AI Systems. Berkeley AI Research Blog Post.
  11. Zhong, Peter Yong; Haoze He; Omar Khattab; Christopher Potts; Matei Zaharia; and Heather Miller. 2024. A guide to Large Language Model abstractions. Two Sigma Blog Post.
  12. Singhvi, Arnav; Manish Shetty; Shangyin Tan; Christopher Potts; Koushik Sen; Matei Zaharia; and Omar Khattab. 2023. DSPy Assertions: Computational constraints for self-refining language model pipelines. arXiv:2312.13382.
  13. Geiger, Atticus; Christopher Potts; and Thomas Icard. 2023. Causal abstraction for faithful model interpretation. Ms., Stanford University.
  14. Khattab, Omar; Keshav Santhanam; Xiang Lisa Li; David Hall; Percy Liang; Christopher Potts; and Matei Zaharia. 2022. Demonstrate–Search–Predict: Composing retrieval and language models for knowledge-intensive NLP. Ms., Stanford University.
  15. Todd, Graham; Shane Steinert-Threlkeld; and Christopher Potts. 2020. Learning compositional negation in populations of Roth-Erev and neural agents. Ms., Stanford University and the University of Washington.
  16. Godefroy, Bruno and Christopher Potts. 2019. Modeling drug–disease relations with linguistic and knowledge graph constraints. Ms, Roam Analytics and Stanford University.
  17. Geiger, Atticus; Ignacio Cases; Lauri Karttunen; and Christopher Potts. 2018. Stress-testing neural models of natural language inference with multiply-quantified sentences. Ms., Stanford University.
  18. Frank, Michael C.; Andrés Gómez Emilsson; Benjamin Peloquin; Noah D. Goodman; and Christopher Potts. 2016. Rational speech act models of pragmatic reasoning in reference games. Ms., Stanford University.
  19. Potts, Christopher. 2013. Conversational implicature: interacting with grammar. Ms., Stanford University, September.
  20. Maas, Andrew L.; Andrew Y. Ng; and Christopher Potts. 2011. Multi-dimensional sentiment analysis with learned representations. Technical report, Stanford Computer Science and Stanford Linguistics, April.
  21. Potts, Christopher. 2008. Indirect answers and cooperation: On Asher and Lascarides's 'Making the right commitments in dialogue'. Commentary paper for the University of Michigan Linguistics and Philosophy Workshop on Implicatures, November.
  22. Potts, Christopher and Florian Schwarz. 2008. Exclamatives and heightened emotion: Extracting pragmatic generalizations from large corpora. Ms, UMass Amherst.
  23. Potts, Christopher. 2008. Interpretive Economy, Schelling Points, and evolutionary stability. Ms, UMass Amherst.
  24. Potts, Christopher. 2008. Pragmatic enrichment via expressive content. Ms, UMass Amherst.
  25. Pater, Joe; Rajesh Bhatt; and Christopher Potts. 2007. Linguistic optimization. Ms., UMass Amherst.

Awards

Fellowships

Invited talks

  1. 2025. Assessing skeptical views of interpretability research. SAIl Faculty Lunch, November 7.
  2. 2025. Why do so many GenAI projects fail? LevelUP 2025 CX Summit. Mountain View, November 6.
  3. 2025. Meaning in large language models: Bridging formal semantics, pragmatics, and learned representations. Seminar series on the 75th Anniversary of the Turing Test, University of Quebec in Montreal, September 25.
  4. 2025. Causal abstraction as a toolkit for developing linguistic theories. Schloss Dagstuhl, July 21.
  5. 2025. Areas of consensus and disagreement in interpretability research. 9th Annual Center for Human-Compatible AI Workshop, June 8.
  6. 2025. Five real-world applications of mechanistic interpretability. Stanford AI Lab Day, April 23.
  7. 2025. Generative AI and the Unexpected Power of Purely Distributional Learning. Propel AI Initiative, University of South Carolina, April 11.
  8. 2025. A Trailer for Mission: Impossible Language Models 2. Department of Linguistics, University of South Carolina, April 10.
  9. 2025. Finding linguistic structure in large language models. Keynote address, Annual Meeting of the Linguistic Society of America, January 11.
  10. 2024. Large Language Models Get the Hype, but Compound Systems Are the Future of AI. Webinar for XCS224u, Stanford University, November 20.
  11. 2024. Building and Training Foundation Models. Artificial Intelligence Training Series for Federal Employees, October 15.
  12. 2024. Finding linguistic structure in large language models. Distinguished Computational Linguistics Lecture, Rochester Institute of Technology, October 4.
  13. 2024. Programming, Not Prompting: Optimizing language model programs with DSPy. HAI Executive Breakfast, July 25.
  14. 2024. Programming, Not Prompting: Optimizing language model programs with DSPy. Amazon Search Experience Day, June 11.
  15. 2024. Three or four stories of unprecedented progress in Artificial Intelligence at Stanford. Stanford Alumni Association, June 3.
  16. 2024. How I learned to stop worrying and love generative AI. Stanford School of Humanitiess & Arts Workshop on Generative AI and teaching, March 20.
  17. 2023. DSPy: Compiling declarative language model calls into self-improving pipelines. Qualcomm Global AI/ML Summit, December 7.
  18. 2023. Achieving faithful, human-interpretable explanations of AI models. Workshop and Lecture Series on the Law and Economics of Innovation, Center for Law And Economics, ETH Zurich, November 15.
  19. 2023. Retrieval-augmented language models for more reliable Web search. Workshop and Lecture Series on the Law and Economics of Innovation, Center for Law And Economics, ETH Zurich, November 14.
  20. 2023. Causal abstraction for faithful, human-interpretable model explanations. Centre for Linguistic Theory and Studies in Probability (CLASP), University of Gothenburg, October 4.
  21. 2023. Characterizing English preposing in PP constructions. English language and linguistic theory: A tribute to Geoff Pullum, University of Edinburgh, August 31.
  22. 2023. Causal abstraction for faithful, human-interpretable model explanations. Neuro-Symbolic AI Summer School 2023, August 30.
  23. 2023. Beyond GPT-3: Key concepts and open questions in a golden age for Natural Language Understanding. Webinar for XCS224u, Stanford University, January 18, 2023.
  24. 2022. Building scalable, performant retrieval-based NLP models using ColBERT. Keynote, 5th National Conference on Multidisciplinary Design, Analysis, and Optimization, IIT Bombay, September 15.
  25. 2022. Building scalable, performant retrieval-based NLP models using ColBERT. Amazon Tech Talk, August 10.
  26. 2022. Lexical semantics in the time of large language models. Keynote, NAACL Workshop: Dimensions of Meaning: Distributional and Curated Semantics, July 14.
  27. 2022. Could a purely self-supervised foundation model achieve grounded language understanding? Naver Labs Europe, June 9.
  28. 2022. Could a purely self-supervised foundation model achieve grounded language understanding? Workshop on Embodied, Situated, and Grounded Intelligence: Implications for AI, Santa Fe Institute, April 15.
  29. 2022. Inducing interpretable causal structures in neural networks. McGill Linguistics and Mila, April 8.
  30. 2022. Socio-pragmatic analyses of functional morphemes. Distinguished Alumnus Address, UCSC Linguistics, March 12.
  31. 2022. Inducing interpretable causal structures in neural networks. UT Austin Linguistics, March 7.
  32. 2022. Inducing interpretable causal structures in neural networks. Linguistics Department, University of Geneva, March 1.
  33. 2021. Benchmark datasets: The essential resources on which all NLP depends. Keynote, Indian Symposium on Machine Learning (IndoML), December 16.
  34. 2021. Socio-pragmatic analyses of functional morphemes. Georgetown Linguistics, December 3.
  35. 2021. Causal abstractions of neural natural language inference models. ILFC Seminar: Interactions between Formal and Computational Linguistics, October 12.
  36. 2021. Causal abstractions of neural networks. Amazon NLP Talk Series, August 31, 2021.
  37. 2021. Rational choices about complex linguistic representations. Approaches to implicature: Rational choice and/or exhaustification, ESSLLI 2021, August 10.
  38. 2021. Improving NLP systems with Questions Under Discussion. Keynote, UnImplicit: The First Workshop on Understanding Implicit and Underspecified Language, August 5.
  39. 2021. Reliable characterizations of NLP systems as a social responsibility. ACL-IJCNLP 2021 keynote address, August 3.
  40. 2021. Towards more meaningful benchmarks for natural language understanding. CMU Language Technology Institute, April 2.
  41. 2020. Panel discussion: ''Should GPT-3 Have the Right to Free Speech?'' COLING 2020, December 9.
  42. 2020. Pragmatic reasoning in large-scale NLP systems. University of Tübingen, December 3.
  43. 2020. Is it possible for language models to achieve language understanding? HAI–OpenAI Workshop on GPT-3, October 14.
  44. 2020. Compositionality or generalization? Princeton Linguistics, September 30.
  45. 2020. Algorithmic-level learning targets for neural Natural Language Inference models. Berkeley NLP Group, August 7.
  46. 2020. Pragmatic reasoning in large-scale NLP systems. CU Boulder, February 21.
  47. 2020. Pragmatic reasoning in large-scale NLP systems. Society for Computation in Linguistics, January 4.
  48. 2019. Towards more meaningful evaluations for natural language understanding. Keynote, TextXD: Text Analysis Across Domains. UC Berkeley, December 4.
  49. 2019. Towards more meaningful evaluations for natural language understanding. Symbolic Systems Forum. Stanford University, November 18
  50. 2019. Fair adversarial tasks for natural language understanding. Cornell Computer Science, November 14.
  51. 2019. Towards more meaningful evaluations for natural language understanding. AI Index Workshop on Measurement in AI Policy. Stanford University, October 20.
  52. 2019. ''Solved'' tasks in natural language processing. ML@SJU. San Jose State University, October 22.
  53. 2019. Compositionality or generalization? Keynote, Linguistic Association of Great Britain, September 9–12.
  54. 2019. ''Solved'' tasks in natural language processing. CURIS Summer Lunch Series, Stanford University, July 16.
  55. 2019. Fair adversarial tasks for natural language understanding. Samsung Research, July 11.
  56. 2019. Linguistic reasoning with deep learning models. McDonnell Focused Workshop: Consistency and variability in relational reasoning: Developmental and comparative perspectives. Stanford, July 5–6.
  57. 2019. Compositionality or generalization? UCLA Linguistics, April 19.
  58. 2019. Compositionality or generalization? Cognition and Language Workshop, Stanford, April 11.
  59. 2018. Resolving semantic uncertainty pragmatically. NYU Linguistics, October 12.
  60. 2018. Language technologies past present, and future. CURIS Summer Lunch Series, Stanford University, July 31.
  61. 2018. Linguists for deep learning; or: How I learned to stop worrying and love neural networks. *Sem, New Orleans, June 5–6.
  62. 2018. Bringing the Rational Speech Acts model to NLP. Symbolic Systems Society Symposium, February 9.
  63. 2017. Enriching distributional linguistic representations with structured resources. Berkeley NLP Group. UC Berkeley, October 30.
  64. 2017. The value of social awareness for general language understanding. Microsoft Research Faculty Summit 2017. Redmond, WA, July 17–18.
  65. 2017. Natural language search into large knowledge graphs. Stanford SDSI and Accenture Workshop. Redwood City, CA, May 9.
  66. 2016. Learning in extended and approximate Rational Speech Acts models. Keynote, Conference on Empirical Methods in Natural Language Processing. Austin, TX, November 1–4.
  67. 2015. Learning in the Rational Speech Acts model. Keynote, Amsterdam Colloquium. December 16–18.
  68. 2015. Negotiating lexical uncertainty and speaker expertise with disjunction. Colloque de Syntaxe et Sémantique á Paris. Paris Diderot, October 8–10.
  69. 2015. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. Johns Hopkins, September 24.
  70. 2015. Coordinating on context and construal. Google, February 19.
  71. 2015. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. Psychology Department, UC Santa Cruz, February 18.
  72. 2015. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. The 41st Meeting of the Berkeley Linguistics Society, February 8.
  73. 2014. Characterizing expressive and social meaning with large corpora. The 14th CUNY Sentence Processing Conference, March 13.
  74. 2013. Conversational implicatures: interacting with grammar. Michigan Philosophy–Linguistics Workshop, November 22–24.
  75. 2013. Conversational implicatures: interacting with grammar. University of Illinois at Urbana-Champaign Linguistics, Oct 28.
  76. 2013. Conversational implicatures: interacting with grammar. UC Berkeley Meaning Sciences Club, October 1.
  77. 2013. Preferences and pragmatic enrichment in multi-agent decision theory. West Point, September 9.
  78. 2013. Preferences and pragmatic enrichment in multi-agent decision theory. Army Research Office, September 5.
  79. 2013. Conversational implicatures: interacting with grammar. UCLA Linguistics colloquium, May 17.
  80. 2013. Preferences and pragmatic enrichment in the Cards dialogue corpus. Princeton Linguistics, May 2.
  81. 2013. Conversational implicature, action, and interaction. Workshop on Actionability, UC Berkeley, April 20.
  82. 2013. Expressives and the limits of compositionality. APA Pacific Division, Symposium on the Semantics and Pragmatics of Pejoratives, San Francisco, March 28.
  83. 2013. Cravin' that straight talk: the latent affective meaning of demonstratives. [Eric Acton and Christopher Potts]. Workshop on Computational Social Sciences, Stanford, January 11.
  84. 2012. The latent affective meaning of demonstratives. [Eric Acton and Christopher Potts]. Workshop on Indexicality, Expressives, and Self-Reference, NASSLLI, Austin, TX, June 22.
  85. 2012. Attribution and discourse representation. Penn Discourse Treebank Workshop, University of Pennsylvania, April 30.
  86. 2012. Relevance and pragmatic enrichment in a task-oriented dialogue corpus. West-Coast Conference on Formal Linguistics 30, UC Santa Cruz, April 13–15.
  87. 2012. Exploring question-driven models of discourse using a task-oriented dialogue corpus. NYU Linguistics, March 23.
  88. 2011. Veridicality and (parenthetical) attitude predications. Workshop on Modification (With and Without Modifiers), Madrid, December 16.
  89. 2011. Practical Sentiment Analysis. Sentiment Analysis Symposium, San Francisco, November 8.
  90. 2011. Exploring question-driven models of discourse using a task-oriented dialogue corpus. University of Rochester Center for Language Sciences, October 13.
  91. 2011. Towards inter-personal, context-dependent sentiment analysis. eBay Research. September 19.
  92. 2011. The emergent expressivity of functional morphemes. Workshop on Expressives and Affective Prosody, University of Salford, Sep 13.
  93. 2011. Synthetic logic characterizations of meanings extracted from large corpora. [Alex Djalali and Christopher Potts]. NSF Workshop on Semantics for Textual Inference. Boulder, CO, July 10.
  94. 2011. Synthetic logic characterizations of meanings extracted from large corpora. [Alex Djalali and Christopher Potts]. Workshop on Natural Logic, Proof Theory, and Computational Semantics. Stanford, CA, April 9.
  95. 2011. On the negativity of negation. Michigan State University, March 17.
  96. 2011. On the negativity of negation. University of Alberta, March 11.
  97. 2010. On the negativity of negation. UC San Diego, October 11.
  98. 2010. Emergent expressivity. Semantic and Linguistic Theory 20, University of Vancouver, April 30.
  99. 2010. Affective demonstratives. UC Berkeley, February 22.
  100. 2010. Uncertainty in indirect answers. UT Austin, February 8.
  101. 2009. Affective demonstratives and the division of pragmatic labor. 17th Amsterdam Colloquium, December 16-18.
  102. 2009. Aligning linguistic alternatives and questions under discussion. Penn Workshop on Situated Understanding of Language, July 23.
  103. 2009. The language of sentiment expression, emotionality, and group cohesion. Workshop on Unified Theories of Language and Cognition, July 8.
  104. 2009. Predicting perspectival orientation for appositives. 45th Annual Chicago Linguistic Society Meeting, April 17.
  105. 2009. Perspective-shifting with appositives and expressives. [Jesse A. Harris and Christopher Potts]. Workshop on Projective Meanings, The Ohio State University, April 2.
  106. 2009. Exclamatives and heightened emotion: Extracting pragmatic generalizations from large corpora. [Christopher Potts and Florian Schwarz]. Computational Linguistics: Implementation of Analyses Against Data, LSA Annual Meeting, January 8-11.
  107. 2008. Indirect answers and cooperation: On Asher and Lascarides's 'Making the right commitments in dialogue'. Commentary paper for the University of Michigan Linguistics and Philosophy Workshop on Implicatures, November 22.
  108. 2008. Expressives in the wild: Extracting pragmatic generalizations from large corpora. [Christopher Potts and Florian Schwarz]. UCSC Alumni Conference, September 12.
  109. 2008. Pragmatic enrichment via expressive content. Stanford, May 23.
  110. 2008. Interpretive Economy, Schelling Points, and evolutionary stability. Stanford Pragmatics Group, May 23.
  111. 2008. The coin of the expressive realm. Arizona Linguistics and Anthropology Symposium, The University of Arizona in Tucson, Arizona, May 10.
  112. 2008. The pragmatics of expressive content. Cornell Workshop on Philosophy of Language, April 26.
  113. 2008. The dynamics of apposition. Chris Barker's NYU Linguistics seminar on dynamics, February 25.
  114. 2007. The generality of pragmatic inference: Message enrichment in multi-agent interactions. U.S. Navy Research Laboratory, December 10.
  115. 2007. Interrogatives: Interpretation and resolution. Alumnus plenary speaker, SUNY-CUNY-NYU 9th Annual Mini-Conference, December 1.
  116. 2007. Questions. [Joint work with Maribel Romero and Jesse Aron Harris]. AAAI 2007 Workshop on Cognitive Approaches to NLP, Arlington, VA, November 10, 2007.
  117. 2007. The compositional independence of expressives. Fifth Barcelona Workshop on Issues in the Theory of Reference, June 6.
  118. 2007. Expressive content and semantic theory. University of Chicago Graduate Workshop in Semantics and Philosophy, April 6.
  119. 2007. An introduction to expressive content. Swarthmore, March 30.
  120. 2007. Pragmatic dimensions: Quality and expressivity. Penn Linguistics, March 29, and University of Chicago, April 5.
  121. 2007. Harmonic Grammar as Linear Programming. Chicago Language Modeling Lab, April 5.
  122. 2007. Harmonic Grammar as Linear Programming. Penn Phonetics Lab, March 29.
  123. 2006. Harmonic Grammar as Linear Programming. UMass Amherst Colloquium, September 22.
  124. 2006. Pragmatic dimensions. Harvard, September 29.
  125. 2006. Pragmatic intrusion of forward-looking utterance modifiers. Adverbes de Phrase Workshop, Université Paris 7, September 15.
  126. 2006. Performatives in the expressive dimension. Groupe Dialogue, GDR Sémantique et Modélisation, Paris, September 14.
  127. 2006. A system of pragmatic pressures. Language for Intelligent Machines Workshop, West Point, July 20.
  128. 2006. Conversational implicatures via general pragmatic pressures. Logic Engineering and Natural Language Semantics 2006, Tokyo, Japan, June 5.
  129. 2006. Integrated pragmatic values. Linguistics, Brown, March 6.
  130. 2006. Integrated pragmatic values. Linguistics, The Ohio State University, February 24.
  131. 2005. Integrated pragmatic values. Linguistics, UCLA, December 1.
  132. 2005. Integrated pragmatic values. Linguistics, Yale University, November 7.
  133. 2005. Integrated pragmatic values. Linguistics, Georg-August University Göttingen, September 22.
  134. 2005. Integrated pragmatic values. Zentrum für Allgemeine Sprachwissenschaft, Berlin, September 21.
  135. 2005. The expressive dimension. Zentrum für Allgemeine Sprachwissenschaft, Berlin, September 23, and Tohoku University, Sendai, June 2.
  136. 2005. Pragmatics from many perspectives. The Harvard Artificial Intelligence Reading Group, April 7.
  137. 2005. Lexicalized intonational meaning. Linguistics at Santa Cruz, March 5, 2005.
  138. 2005. Lexicalized intonational meaning. Workshop on the (In)-Determinacy of Meaning, Deutsche Gesellschaft für Sprachwissenschaft, February 23.
  139. 2004. The dimensions of quotation. The Harvard Workshop on Indexicality, November 20.
  140. 2004. The multidimensionality of expressive content. Rutgers University, November 5.
  141. 2004. Lexicalized intonational meaning. University of Maryland, October 1.
  142. 2004. Honorifics: Interpreted and interpretable. [Ash Asudeh and Christopher Potts]. The Phi Workshop, McGill University, August 29.
  143. 2003. The performative nature of expressive content. University of Rochester, November 18.
  144. 2003. The performative nature of expressive content. The Ling Lunch, University of Connecticut, November 17.
  145. 2003. Keeping world and will apart: A discourse-based semantics for imperatives. New York University Syntax/Semantics Lecture Series, October 17.
  146. 2003. A layered semantics for utterance modifiers. The Workshop on Direct Compositionality, Brown University, June 21.
  147. 2003. Conventional implicatures, a distinguished class of meanings. USC, February 13.
  148. 2003. Conventional implicatures, a distinguished class of meanings. UMass Amherst, February 10.
  149. 2002. A new factual basis for conventional implicatures. The Ling Lunch, University of Connecticut, October 29.
  150. 2002. Model theory and output–output correspondence. [Christopher Potts and Geoffrey K. Pullum]. The Phonology Workshop, Stanford, October 17.
  151. 2002. Comparative economy conditions in natural language syntax. The North American Summer School in Logic, Language, and Information 1, Workshop on Model-Theoretic Syntax, Stanford, June 28.
  152. 2002. A description language for economy conditions. The Syntax Workshop, Stanford, January 29.

Teaching

  1. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2025)
  2. Linguist 197a: Undergraduate research seminar (senior capstone course; undergraduate, Stanford, Winter 2025)
  3. Linguist 395: Research workshop (graduate, Stanford, Spring 2025)
  4. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Fall 2024)
  5. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2024)
  6. Linguist 197a: Undergraduate research seminar (senior capstone course; undergraduate, Stanford, Winter 2024)
  7. Linguist 395: Research workshop (graduate, Stanford, Spring 2024)
  8. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Winter 2024)
  9. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Fall 2023)
  10. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2023)
  11. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2023)
  12. Linguist 197a: Undergraduate research seminar (senior capstone course; undergraduate, Stanford, Winter 2023)
  13. Linguist 230: Advanced semantics and pragmatics (undergraduate and graduate, Stanford, Fall 2022)
  14. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Winter 2023)
  15. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Fall 2022)
  16. NYI 2023 Puzzle Series: The inferential properties of quantificational determiners (New York Institute, Winter 2023)
  17. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2022)
  18. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2022)
  19. Linguist 197a: Undergraduate research seminar (senior capstone course; undergraduate, Stanford, Winter 2022)
  20. Linguist 278: Programming for linguists (undergraduate and graduate, Stanford, Fall 2021)
  21. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Winter 2022)
  22. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Fall 2021)
  23. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2021; with Bill MacCartney)
  24. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2021)
  25. Linguist 278: Programming for linguists (undergraduate and graduate, Stanford, Fall 2020)
  26. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Fall 2020)
  27. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Winter 2021)
  28. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2020; with Bill MacCartney)
  29. XCS 224u, Natural language understanding (online course, Stanford Center for Professional Development, Winter–Spring 2020)
  30. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2020)
  31. Linguist 278: Programming for Linguists (undergraduate and graduate, Stanford, Fall 2019)
  32. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2019; with Bill MacCartney)
  33. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2019)
  34. Linguist 278: Programming for Linguists (undergraduate and graduate, Stanford, Fall 2018)
  35. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2018; with Bill MacCartney)
  36. Linguist 230b: Advanced semantics (graduate, Stanford, Spring 2018)
  37. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2018)
  38. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2016; with Bill MacCartney)
  39. Linguist 230b: Advanced semantics (graduate, Stanford, Spring 2016)
  40. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2016)
  41. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2015; with Bill MacCartney)
  42. Linguist 236: Reasoning with quantifiers (graduate, Stanford, Spring 2015; with Dan Lassiter)
  43. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2015)
  44. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Spring 2014; with Bill MacCartney)
  45. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2014)
  46. Linguist 278: Programming for Linguists (graduate, Stanford, Fall 2013)
  47. Linguist 230d: Semantics and Pragmatics Group (graduate, Stanford, Fall 2013 – Spring 2014; with Dan Lassiter and Beth Levin)
  48. Linguist 236 / Psychology 236c: Representations of meaning (graduate, Stanford, Spring 2013; with Noah D. Goodman)
  49. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2013)
  50. CS 224U / Linguist 188/288: Natural language understanding (undergraduate and graduate, Stanford, Winter 2013; with Bill MacCartney)
  51. Linguist 395: Research workshop (graduate, Stanford, Spring 2013)
  52. Linguist 230d: Semantics and Pragmatics Group (graduate, Stanford, Fall 2012 – Spring 2013; with Beth Levin)
  53. Extracting social meaning and sentiment. North American Summer Schoool in Logic, Language, and Information, UT Austin, June.
  54. Linguist 230b: Advanced semantics and pragmatics (graduate, Stanford, Spring 2012)
  55. Linguist 236 / Psychology 236c: Context dependence in language and communication (graduate, Stanford, Spring 2012; with Mike Frank)
  56. CS 224u/Linguist 188/Linguist 288: Natural Language Understanding (undergraduate and graduate, Stanford, Winter 2012; with Bill MacCartney)
  57. Linguist 130a/230a: Introduction to semantics and pragmatics (undergraduate and graduate, Stanford, Winter 2012)
  58. Ling 7800-007: Computational Pragmatics (LSA Linguistic Institute, University of Colorado, July 2011).
  59. Linguist 130a: Introduction to linguistic meaning (undergraduate, Stanford, Winter 2011)
  60. Linguist 230a: Introduction to semantics and pragmatics (graduate, Stanford, Fall 2010)
  61. Linguist 287 / CS 424p: Extracting social meaning and sentiment (undergraduate and graduate, Stanford, Fall 2009; with Dan Jurafsky)
  62. Linguist 239: Semantics Research Seminar (graduate, Stanford, Fall 2010–Winter 2011; with Beth Levin)
  63. Linguist 130a: Introduction to Linguistic Meaning (undergraduate, Stanford, Spring 2010)
  64. Linguist 230a: Introduction to Semantics and Pragmatics (graduate, Stanford, Winter 2010)
  65. Linguist 236: Lexical and Constructional Pragmatics (graduate, Stanford, Fall 2009)
  66. Linguist 239: Semantics Research Seminar (graduate, Stanford, Fall 2009–Spring 2010; with Beth Levin)
  67. Linguist 278: Programming for Linguists (graduate, Stanford, Fall 2009)
  68. Ling 380/390A: Controlling the Discourse (undergraduate, UMass Amherst, Spring 2009; with Lyn Frazier).
  69. Ling 409: Mathematical Methods in Linguistics (undergraduate, UMass Amherst, Spring 2009; with Rajesh Bhatt).
  70. Ling 610: Semantics and Generative Grammar (graduate, UMass Amherst, Fall 2008).
  71. Ling 797A: Third-Year Seminar (graduate, UMass Amherst, Fall 2008).
  72. Ling 753: Conversational Inference (graduate, UMass Amherst, Spring 2008).
  73. Undergraduate independent study: Seth Garrison: Contextual understanding of denominal verbs (UMass Amherst, Spring 2008).
  74. Undergraduate independent study: Yelena Pashchenko: Generalized conversational implicatures (UMass Amherst, Spring 2008)
  75. Ling 390a: Controlling the Discourse (undergraduate, UMass Amherst, Fall 2007).
  76. LSA.314: Dimensions of Meaning (LSA Linguistic Institute, Stanford, July 2007).
  77. LSA.374: Conversational Inference (LSA Linguistic Institute, Stanford, July 2007; with David Beaver and Robert van Rooij).
  78. LSA.108P: Logic for Linguists (LSA Linguistic Institute, Stanford, July 2007).
  79. Ling 620: Formal Semantics (graduate, UMass Amherst, Spring 2007).
  80. Ling 201: Introduction to Linguistics (undergraduate, UMass Amherst, Spring 2007).
  81. Ling 510: Introduction to Semantics (undergraduate, UMass Amherst, Spring 2006).
  82. Ling 390a: Controlling the Discourse (undergraduate, UMass Amherst, Spring 2006; with Lyn Frazier).
  83. Ling 496: Pragmatics (undergraduate, UMass Amherst, Spring 2006).
  84. Ling 610: Semantics and Generative Grammar (graduate, UMass Amherst, Fall 2005).
  85. Introduction to Pragmatics (two-week course at the New York Institute of Cognitive and Cultural Studies, St. Petersburg State University, July 2005).
  86. Introduction to Semantics (two-week course at the New York Institute of Cognitive and Cultural Studies, St. Petersburg State University, July 2005).
  87. Ling 394a: Controlling the Discourse (undergraduate, UMass Amherst, Spring 2005; with Lyn Frazier).
  88. Ling 753: Seminar on Modes of Semantic Composition (graduate, UMass Amherst, Spring 2005).
  89. Ling 201: Introduction to Linguistics (undergraduate, UMass Amherst, Fall 2004).
  90. Ling 610: Semantics and Generative Grammar (graduate, UMass Amherst, Fall 2004).
  91. Ling 797A: Third-Year Seminar (graduate, UMass Amherst, Fall 2004).
  92. Introduction to Pragmatics (two-week course at the New York Institute of Cognitive and Cultural Studies, St. Petersburg State University, July 2004).
  93. Controlling the Discourse (two-week course at the New York Institute of Cognitive and Cultural Studies, St. Petersburg State University, July 2004).
  94. Ling 401: Introduction to Syntax, undergraduate, UMass Amherst, Spring 2004.
  95. Ling 620: Formal Semantics, graduate, UMass Amherst, Spring 2004.
  96. Ling 720: Proseminar in Semantics and Formal Pragmatics (graduate), UMass Amherst, Fall 2003.
  97. Writing I (undergraduate), Writing Program, UCSC, Spring 2003.
  98. Semantics II (undergraduate), Department of Linguistics, UCSC, Spring 2002.

Professional service

University service

Departmental service

General

Postdoc advisor

Dissertation advisor

Generals, Qualifying, and Masters papers

Stanford

UMass Amherst

Undergraduate theses (primary advisor)

Other skills