Hewitt, John

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

Lost in the Middle: How Language Models Use Long Contexts

Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,612

Lost in the Middle: How Language Models Use Long Contexts

Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,599

Designing and Interpreting Probes with Control Tasks

Hewitt, J., & Liang, P. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 393

Designing and Interpreting Probes with Control Tasks

Hewitt, J., & Liang, P. (2019) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 110

The EOS Decision and Length Extrapolation

Newman, B., Hewitt, J., Liang, P., & Manning, C. D. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Model Editing with Canonical Examples

Hewitt, J., Chen, S., Xie, L. L., Adams, E., Liang, P., & Manning, C. D. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 1

Because we have LLMs, we Can and Should Pursue Agentic Interpretability

Kim, B., Hewitt, J., Nanda, N., Fiedel, N., & Tafjord, O. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 1