Potts, Christopher

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 6,878

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 3,377

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 911

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Information Retrieval and Search BehaviorTopic Modeling

Cited by 353

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 254

Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

Opsahl-Ong, K., Ryan, M. J., Purtell, J., Broman, D., Potts, C., Zaharia, M., & Khattab, O. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 212

ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems

Saad-Falcon, J., Khattab, O., Potts, C., & Zaharia, M. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 148

Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding

Monroe, W., Hawkins, R. X. D., Goodman, N. D., & Potts, C. (2017) · Transactions of the Association for Computational Linguistics

Categorization, perception, and languageSyntax, Semantics, Linguistic Variation

Cited by 94

Quantifying large language model usage in scientific papers

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour

scientometrics and bibliometrics researchAcademic Writing and Publishing

Cited by 80

DynaSent: A Dynamic Benchmark for Sentiment Analysis

Potts, C., Wu, Z., Geiger, A., & Kiela, D. (2021) · 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)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 56

MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

Zhong, Z., Wu, Z., Manning, C. D., Potts, C., & Chen, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 55

Mapping the Increasing Use of LLMs in Scientific Papers

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2024) · arXiv

scientometrics and bibliometrics researchAcademic Writing and Publishing

Cited by 55

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., & Zaharia, M. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 52

Causal abstractions of neural networks

Geiger, A., Lu, H., Icard, T., & Potts, C. (2021) · arXiv

Explainable Artificial Intelligence (XAI)Bayesian Modeling and Causal Inference

Cited by 45

An Incremental Iterated Response Model of Pragmatics

Cohn-Gordon, R., Goodman, N., & Potts, C. (2019) · University of Massachusetts Amherst

Syntax, Semantics, Linguistic VariationLogic, Reasoning, and Knowledge

Cited by 26

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Kreiss, E., Bennett, C., Hooshmand, S., Zelikman, E., Ringel Morris, M., & Potts, C. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsDigital Accessibility for Disabilities

Cited by 24

Postmortem memory of public figures in news and social media

West, R., Leskovec, J., & Potts, C. (2021) · Proceedings of the National Academy of Sciences

Memory, Trauma, and CommemorationDeath, Funerary Practices, and Mourning

Cited by 15

Interpretability at Scale: Identifying Causal Mechanisms in Alpaca

Wu, Z., Geiger, A., Icard, T., Potts, C., & Goodman, N. D. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Philosophy of Biology and Scientific Explanation

Cited by 14

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

Geiger, A., Ibeling, D., Zur, A., Chaudhary, M., Chauhan, S., Huang, J., Arora, A., Wu, Z., Goodman, N., Potts, C., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 11

ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems

Saad-Falcon, J., Khattab, O., Potts, C., & Zaharia, M. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 7

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Agrawal, L. A., Tan, S., Soylu, D., Ziems, N., Khare, R., Opsahl-Ong, K., Singhvi, A., Shandilya, H., Ryan, M. J., Jiang, M., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

Opsahl-Ong, K., Ryan, M. J., Purtell, J., Broman, D., Potts, C., Zaharia, M., & Khattab, O. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4