Bowman, Samuel R.

The Capacity for Moral Self-Correction in Large Language Models

Ganguli, D., Askell, A., Schiefer, N., Liao, T. I., Lukošiūtė, K., Chen, A., Goldie, A., Mirhoseini, A., Olsson, C., Hernandez, D., et al. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 53

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 51

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 46

Measuring Progress on Scalable Oversight for Large Language Models

Bowman, S. R., Hyun, J., Perez, E., Chen, E., Pettit, C., Heiner, S., Lukošiūtė, K., Askell, A., Jones, A., Chen, A., et al. (2022) · arXiv

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 34

Measuring Faithfulness in Chain-of-Thought Reasoning

Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., et al. (2023) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 33

GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J., & Bowman, S. R. (2023) · arXiv

Topic ModelingExpert finding and Q&A systems

Cited by 30

Alignment faking in large language models

Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 30

Inverse Scaling: When Bigger Isn't Better

McKenzie, I. R., Lyzhov, A., Pieler, M., Parrish, A., Mueller, A., Prabhu, A., McLean, E., Kirtland, A., Ross, A., Liu, A., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

BLiMP: the benchmark of linguistic minimal pairs for English

Warstadt, A., Parrish, A., Liu, H., Mohananey, A., Peng, W., Wang, S.-F., & Bowman, S. R. (2020) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 28

Studying Large Language Model Generalization with Influence Functions

Grosse, R., Bae, J., Anil, C., Elhage, N., Tamkin, A., Tajdini, A., Steiner, B., Li, D., Durmus, E., Perez, E., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 26

Many-shot Jailbreaking

Anil, C., Durmus, E., Panickssery, N., Sharma, M., Benton, J., Kundu, S., Batson, J., Tong, M., Mu, J., Ford, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 25

Pretraining Language Models with Human Preferences

Korbak, T., Shi, K., Chen, A., Bhalerao, R., Buckley, C. L., Phang, J., Bowman, S. R., & Perez, E. (2023) · arXiv

Machine Learning and AlgorithmsInnovative Teaching and Learning Methods

Cited by 23

Comparing Test Sets with Item Response Theory

Vania, C., Htut, P. M., Huang, W., Mungra, D., Pang, R. Y., Phang, J., Liu, H., Cho, K., & Bowman, S. R. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 18

Reasoning Models Don't Always Say What They Think

Chen, Y., Benton, J., Radhakrishnan, A., Uesato, J., Denison, C., Schulman, J., Somani, A., Hase, P., Wagner, M., Roger, F., et al. (2025) · arXiv

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

Cited by 18

Debating with More Persuasive LLMs Leads to More Truthful Answers

Khan, A., Hughes, J., Valentine, D., Ruis, L., Sachan, K., Radhakrishnan, A., Grefenstette, E., Bowman, S. R., Rocktäschel, T., & Perez, E. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models

Denison, C., MacDiarmid, M., Barez, F., Duvenaud, D., Kravec, S., Marks, S., Schiefer, N., Soklaski, R., Tamkin, A., Kaplan, J., et al. (2024) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 11

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2021) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 10

Debate Helps Supervise Unreliable Experts

Michael, J., Mahdi, S., Rein, D., Petty, J., Dirani, J., Padmakumar, V., & Bowman, S. R. (2023) · arXiv

Epistemology, Ethics, and MetaphysicsExpert finding and Q&A systems

Cited by 8

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning

Radhakrishnan, A., Nguyen, K., Chen, A., Chen, C., Denison, C., Hernandez, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S. R., & Cho, K. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Sharma, M., Tong, M., Mu, J., Wei, J., Kruthoff, J., Goodfriend, S., Ong, E., Peng, A., Agarwal, R., Anil, C., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 7

Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Pfau, J., Merrill, W., & Bowman, S. R. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

Language Models Learn to Mislead Humans via RLHF

Wen, J., Zhong, R., Khan, A., Perez, E., Steinhardt, J., Huang, M., Bowman, S. R., He, H., & Feng, S. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 4

Auditing language models for hidden objectives

Marks, S., Treutlein, J., Bricken, T., Lindsey, J., Marcus, J., Mishra-Sharma, S., Ziegler, D., Ameisen, E., Batson, J., Belonax, T., et al. (2025) · arXiv

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

Cited by 4