Goodman, Noah D.

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

How to grow a mind: Statistics, structure, and abstraction

Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science

Bayesian Modeling and Causal InferenceCategorization, perception, and language

Cited by 1,815

Predicting pragmatic reasoning in language games

Frank, M. C., & Goodman, N. D. (2012) · Science

Syntax, Semantics, Linguistic VariationCategorization, perception, and language

Cited by 868

Pragmatic Language Interpretation as Probabilistic Inference

Goodman, N. D., & Frank, M. C. (2016) · Trends in Cognitive Sciences

Syntax, Semantics, Linguistic VariationLanguage Development and Disorders

Cited by 594

Church: a language for generative models

Goodman, N., Mansinghka, V., Roy, D. M., Bonawitz, K., & Tenenbaum, J. B. (2012) · arXiv

Bayesian Modeling and Causal InferenceLogic, programming, and type systems

Cited by 577

A rational analysis of rule-based concept learning

Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008) · Cognitive Science

Categorization, perception, and languageBayesian Modeling and Causal Inference

Cited by 469

The anchoring bias reflects rational use of cognitive resources

Lieder, F., Griffiths, T. L., M. Huys, Q. J., & Goodman, N. D. (2018) · Psychonomic Bulletin & Review

Decision-Making and Behavioral EconomicsCognitive Science and Education Research

Cited by 230

Multimodal Generative Models for Scalable Weakly-Supervised Learning

Wu, M., & Goodman, N. (2018) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 200

STaR: Bootstrapping Reasoning With Reasoning

Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022) · arXiv

Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

Cited by 182

The language of generalization

Tessler, M. H., & Goodman, N. D. (2019) · Psychological Review

Categorization, perception, and languageSyntax, Semantics, Linguistic Variation

Cited by 101

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

When redundancy is useful: A Bayesian approach to “overinformative” referring expressions

Degen, J., Hawkins, R. D., Graf, C., Kreiss, E., & Goodman, N. D. (2020) · Psychological Review

Syntax, Semantics, Linguistic VariationCategorization, perception, and language

Cited by 90

On scalable oversight with weak LLMs judging strong LLMs

Kenton, Z., Siegel, N. Y., Kramár, J., Brown-Cohen, J., Albanie, S., Bulian, J., Agarwal, R., Lindner, D., Tang, Y., Goodman, N. D., et al. (2024) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 66

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Wong, L., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., & Tenenbaum, J. B. (2023) · arXiv

Logic, Reasoning, and KnowledgeNatural Language Processing Techniques

Cited by 36

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

Understanding Social Reasoning in Language Models with Language Models

Gandhi, K., Fränken, J.-P., Gerstenberg, T., & Goodman, N. D. (2023) · arXiv

Child and Animal Learning DevelopmentTopic Modeling

Cited by 23

Solving Math Word Problems by Combining Language Models With Symbolic Solvers

He-Yueya, J., Poesia, G., Wang, R. E., & Goodman, N. D. (2023) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 16

Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Zelikman, E., Harik, G., Shao, Y., Jayasiri, V., Haber, N., & Goodman, N. D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 16

Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions

Zelikman, E., Huang, Q., Poesia, G., Goodman, N., & Haber, N. (2023) · Advances in Neural Information Processing Systems 36

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

Cited by 15

Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions

Zelikman, E., Huang, Q., Poesia, G., Goodman, N. D., & Haber, N. (2022) · arXiv

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

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

The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use

Peloquin, B. N., Goodman, N. D., & Frank, M. C. (2020) · Topics in Cognitive Science

Language and cultural evolutionSyntax, Semantics, Linguistic Variation

Cited by 11

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

Multimodal Generative Models for Compositional Representation Learning

Wu, M., & Goodman, N. (2019) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 8