Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. - Theodore R. Sumers; Mark K. Ho; Tom Griffiths 001 (CogSci, 2020)

Learning Rewards From Linguistic Feedback. - Theodore R. Sumers; Mark K. Ho; Robert X. D. Hawkins; Karthik Narasimhan; Thomas L. Griffiths 001 (AAAI, 2021)

Extending rational models of communication from beliefs to actions. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Tom Griffiths 001 (CogSci, 2021)

Playing the Lottery of a Lifetime: The Effect of Socially Induced Aspiration on Q-Learning Agents. - Yosi Hatekar; Rachit Dubey; Theodore R. Sumers; Ilia Sucholutsky (CogSci, 2022)

Predicting Human Similarity Judgments Using Large Language Models. - Raja Marjieh; Ilia Sucholutsky; Theodore R. Sumers; Nori Jacoby; Tom Griffiths 001 (CogSci, 2022)

How to talk so AI will learn: Instructions, descriptions, and autonomy. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Tom Griffiths 001; Dylan Hadfield-Menell (NeurIPS, 2022)

Linguistic communication as (inverse) reward design. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Thomas L. Griffiths 001; Dylan Hadfield-Menell (CoRR, 2022)

Words are all you need? Capturing human sensory similarity with textual descriptors. - Raja Marjieh; Pol van Rijn; Ilia Sucholutsky; Theodore R. Sumers; Harin Lee; Thomas L. Griffiths 001; Nori Jacoby (CoRR, 2022)

How to talk so your robot will learn: Instructions, descriptions, and pragmatics. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Thomas L. Griffiths 001; Dylan Hadfield-Menell (CoRR, 2022)

Words are all you need? Language as an approximation for human similarity judgments. - Raja Marjieh; Pol van Rijn; Ilia Sucholutsky; Theodore R. Sumers; Harin Lee; Thomas L. Griffiths 001; Nori Jacoby (ICLR, 2023)

Distilling Internet-Scale Vision-Language Models into Embodied Agents. - Theodore R. Sumers; Kenneth Marino; Arun Ahuja; Rob Fergus; Ishita Dasgupta 001 (ICML, 2023)

Deep de Finetti: Recovering Topic Distributions from Large Language Models. - Liyi Zhang; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2023)

Preference-Conditioned Language-Guided Abstraction. - Andi Peng; Andreea Bobu; Belinda Z. Li; Theodore R. Sumers; Ilia Sucholutsky; Nishanth Kumar; Thomas L. Griffiths 001; Julie A. Shah (HRI, 2024)

Learning with Language-Guided State Abstractions. - Andi Peng; Ilia Sucholutsky; Belinda Z. Li; Theodore R. Sumers; Thomas L. Griffiths 001; Jacob Andreas; Julie Shah (ICLR, 2024)

How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? - Ryan Liu 001; Theodore R. Sumers; Ishita Dasgupta 001; Thomas L. Griffiths 001 (ICML, 2024)

Representational Alignment Supports Effective Machine Teaching. - Ilia Sucholutsky; Katherine M. Collins; Maya Malaviya; Nori Jacoby; Weiyang Liu; Theodore R. Sumers; Michalis Korakakis; Umang Bhatt; Mark K. Ho; Joshua B. Tenenbaum; Bradley C. Love; Zachary A. Pardos; et al. (CoRR, 2024)

Rational Metareasoning for Large Language Models. - Nicolò De Sabbata; Theodore R. Sumers; Thomas L. Griffiths 001 (CoRR, 2024)

Clio: Privacy-Preserving Insights into Real-World AI Use. - Alex Tamkin; Miles McCain; Kunal Handa; Esin Durmus; Liane Lovitt; Ankur Rathi; Saffron Huang; Alfred Mountfield; Jerry Hong; Stuart Ritchie; Michael Stern; Brian Clarke; et al. (CoRR, 2024)

Cognitive Architectures for Language Agents. - Theodore R. Sumers; Shunyu Yao; Karthik Narasimhan; Thomas L. Griffiths 001 (Trans. Mach. Learn. Res., 2024)

What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. - Liyi Zhang; Michael Y. Li; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (Trans. Mach. Learn. Res., 2025)

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming. - Mrinank Sharma; Meg Tong; Jesse Mu; Jerry Wei; Jorrit Kruthoff; Scott Goodfriend; Euan Ong; Alwin Peng; Raj Agarwal; Cem Anil; Amanda Askell; Nathan Bailey; et al. (CoRR, 2025)