Thomas L. Griffiths — all works
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models ↗arXiv:2305.10601 · no summary yetShunyu Yao et al.2023 · 2,671 citations
- Hierarchical Topic Models and the Nested Chinese Restaurant Process ↗no summary yetThomas L. Griffiths et al.2003 · 986 citations
- The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies ↗Journal of the ACM · no summary yetDavid M. Blei, Thomas L. Griffiths, Michael I. Jordan2007 · 675 citations
- Integrating Topics and Syntax ↗no summary yetThomas L. Griffiths et al.2004 · 546 citations
- A fully Bayesian approach to unsupervised part-of-speech taggingAnnual Meeting of the Association for Computational Linguistics (ACL)Sharon Goldwater, Tom Griffiths2007 · 382 citations
- Contextual Dependencies in Unsupervised Word SegmentationAnnual Meeting of the Association for Computational Linguistics (ACL)Sharon Goldwater, Thomas L. Griffiths, Mark Johnson2006 · 373 citations
- Integrating explanation and prediction in computational social science ↗Nature · no summary yetJake M. Hofman et al.2021 · 370 citations
- Bayesian Inference for PCFGs via Markov Chain Monte CarloConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Mark Johnson, Thomas Griffiths, Sharon Goldwater2007 · 353 citations
- Human Uncertainty Makes Classification More Robust ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetJoshua C. Peterson et al.2019 · 228 citations
- Cognitive Architectures for Language Agents ↗arXiv:2309.02427 · no summary yetTheodore R. Sumers et al.2023 · 189 citations
- Automated reconstruction of ancient languages using probabilistic models of sound change ↗Proceedings of the National Academy of Sciences · no summary yetAlexandre Bouchard‐Côté et al.2013 · 177 citations
- Recasting Gradient-Based Meta-Learning as Hierarchical Bayes ↗arXiv (Cornell University) · no summary yetErin Grant et al.2018 · 165 citations
- Evaluating Theory of Mind in Question Answering ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetAida Nematzadeh et al.2018 · 114 citations
- Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve ↗arXiv:2309.13638 · no summary yetR. Thomas McCoy et al.2023 · 111 citations
- A Probabilistic Approach to Diachronic Phonology ↗Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) · no summary yetAlexandre Bouchard et al.2007 · 78 citations
- Scaling up psychology via Scientific Regret Minimization ↗Proceedings of the National Academy of Sciences · no summary yetMayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths2020 · 61 citations
- Improving Interpersonal Communication by Simulating Audiences with Language Models ↗arXiv (Cornell University) · no summary yetRyan Liu et al.2023 · 13 citations
- Localized Cultural Knowledge is Conserved and Controllable in Large Language ModelsFindings of the Association for Computational LinguisticsVeniamin Veselovsky, Berke Argın, Benedikt Stroebl, et al.2026 · 7 citations
- On Benchmarking Human-Like Intelligence in Machines ↗arXiv:2502.20502 · no summary yetLance Ying et al.2025 · 7 citations
- When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1 ↗arXiv (Cornell University) · no summary yetRichard Thomas McCoy et al.2024 · 7 citations
- A pragmatic account of the weak evidence effectOpen MindSamuel A. Barnett, Thomas L. Griffiths, Robert D. Hawkins2021 · 7 citations
- Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems ↗arXiv:2505.17968 · no summary yetJiayi Geng et al.2025 · 4 citations
- Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models ↗arXiv (Cornell University) · no summary yetLiang, Kaiqu et al.2025 · 3 citations
- Whither symbols in the era of advanced neural networks? ↗Trends in Cognitive Sciences · no summary yetThomas L. Griffiths et al.2026 · 2 citations
- Visually-Grounded Bayesian Word Learning ↗UC Berkeley · no summary yetYangqing Jia et al.2012
- \tThe nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies ↗no summary yetDavid M. Blei, Thomas L. Griffiths, Michael I. Jordan2010
- Hierarchical topic models and the nested Chinese restaurant process. NIPS ↗no summary yetDavid M. Blei et al.2004
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