Publications
Self-Steering Language Models.
Gabriel Grand, Joshua B. Tenenbaum, Vikash K. Mansinghka, Alexander K. Lew, Jacob Andreas. ArXiV: 2504.07081 (2025).
[arXiv]
[GitHub]
Featured on 🤗 Daily Papers
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo.
João Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O’Donnell. International Conference on Learning Representations (ICLR, 2025).
[OpenReview]
[GitHub]
Oral Talk, ICLR 2025
Stream of Search (SoS): Learning to Search in Language.
Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, Noah D. Goodman. Conference on Language Models (COLM, 2024).
[arXiv]
[GitHub]
Oral Spotlight, COLM 2024
A Llama Sunk My Battleship! Asking Rational Questions with LLMs via Bayesian Inference.
Gabriel Grand, Valerio Pepe, Jacob Andreas, Joshua B. Tenenbaum. System-2 Reasoning at Scale (NeurIPS, 2024).
[arXiv]
[GitHub]
Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling.
Gabriel Grand, Valerio Pepe, Jacob Andreas, Joshua B. Tenenbaum. ArXiV: 2402.19471 (2024).
[arXiv]
[GitHub]
LILO: Learning Interpretable Libraries by Compressing and Documenting Code.
Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas. International Conference on Learning Representations (ICLR, 2024).
[arXiv]
[GitHub]
Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models
.
Anna A. Ivanova, Aalok Sathe, Benjamin Lipkin, Unnathi Kumar, Setayesh Radkani, Thomas H. Clark, Carina Kauf, Jennifer Hu, R.T. Pramod, Gabriel Grand, Vivian Paulun, Maria Ryskina, Ekin Akyürek, Ethan Wilcox, Nafisa Rashid, Leshem Choshen, Roger Levy, Evelina Fedorenko, Joshua Tenenbaum, Jacob Andreas. ArXiV: 2405.09605 (2024).
[arXiv]
[GitHub]
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs.
Alexander K Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K Mansinghka. ArXiV: 2306.03081 (2023).
[arXiv]
[GitHub]
[Docs]
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought.
Lionel Wong*, Gabriel Grand*, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum. ArXiV: 2306.12672 (2023).
[arXiv]
[GitHub]
Evaluating Statistical Language Models as Pragmatic Reasoners.
Benjamin Lipkin, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum. Proceedings of the Annual Meeting of the Cognitive Science Society (2023).
[CogSci]
[arXiv]
[GitHub]
Grounded Physical Language Understanding with Probabilistic Programs and Simulated Worlds
Cedegao Zhang, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum. Proceedings of the Annual Meeting of the Cognitive Science Society (2023).
[CogSci]
[GitHub]
Top-Down Synthesis for Library Learning.
Matthew Bowers, Theo X. Olausson, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum, Kevin Ellis, Armando Solar-Lezama. Proceedings of the ACM on Programming Languages, Volume 7, Issue POPL (Jan 2023).
[ACM Digital Library]
[arXiv]
[GitHub]
“Semantic projection” recovers rich human knowledge of multiple object features from word embeddings.
Gabriel Grand, Idan Blank, Francisco Pereira, and Evelina Fedorenko. Nature Human Behaviour (2022).
[Nature]
[MIT McGovern Institute]
[arXiv]
Identifying concept libraries from language about object structure.
Catherine Wong*, William P. McCarthy*, Gabriel Grand*, Yoni Friedman, Joshua B. Tenenbaum, Jacob Andreas, Robert D. Hawkins, and Judith E. Fan. CogSci (2022).
[arXiv]
[website]
[GitHub]
ChemBERTa-2: Towards Chemical Foundation Models.
Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. ELLIS ML for Molecules Worshop (December, 2021).
[paper]
[workshop]
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. NeurIPS ML for Molecules Worshop (December, 2020).
[arXiv]
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects. Gabriel Grand and Yonatan Belinkov. Proceedings of the 2nd Workshop on Shortcomings in Vision and Language (SiVL) at NAACL-HLT, Minneapolis, MN (June, 2019).
[arXiv]
[ACL]
Best Paper Award, SiVL Workshop, NAACL 2019
Learning Interpretable and Bias-Free Models for Visual Question Answering. Gabriel Grand. Harvard Undergraduate Thesis, advised by Alexander Rush and presented to the Department of Computer Science (2018).
[PDF]
Hoopes Prize
On the Flip Side: Identifying Counterexamples in Visual Question Answering. Gabriel Grand, Aron Szanto, Yoon Kim, and Alexander Rush. KDD Deep Learning Day, London, UK (August, 2018).
[arXiv]
[KDD]