Jacob Andreas

Jacob Andreas is an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research aims to understand the computational foundations of efficient language learning, and build general-purpose intelligent systems that can communicate effectively with humans and learn from human guidance. He earned a BS from Columbia University and an MPhil from the University of Cambridge, where he studied as a Churchill Scholar. He earned a PhD from the University of California, Berkeley.

Selected Publications

• Akyürek, E., Damani, M., Zweiger, A., Qiu, L., Guo, H., Pari, J., Kim, Y., & Andreas, J. (2025). The surprising effectiveness of test‑time training for few‑shot learning. Proceedings of the 42nd International Conference on Machine Learning (ICML).
• Hou, B., Zhang, Y., Andreas, J., & Chang, S. (2025). A probabilistic framework for LLM hallucination detection via belief tree propagation. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 3076–3099).
• Akyürek, A. F., Akyürek, E., Choshen, L., Wijaya, D., & Andreas, J. (2024). Deductive closure training of language models for coherence, accuracy, and updatability. Findings of the Association for Computational Linguistics: ACL 2024 (pp. 9802–9818).

Media

• July 8, 2025: MIT News, Study could lead to LLMs that are better at complex reasoning
• July 23, 2024: MIT News, MIT researchers advance automated interpretability in AI models
• July 11, 2024: MIT News, Reasoning skills of large language models are often overestimated