Koh, Pang Wei

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

Mobility network models of COVID-19 explain inequities and inform reopening

Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J. (2021) · Nature

COVID-19 epidemiological studiesMathematical and Theoretical Epidemiology and Ecology Models

Cited by 1,533

Understanding Black-box Predictions via Influence Functions

Koh, P. W., & Liang, P. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 1,514

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Data Classification

Cited by 518

Certified Defenses for Data Poisoning Attacks

Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 479

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 407

Concept Bottleneck Models

Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., & Liang, P. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Machine Learning in Healthcare

Cited by 338

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 306

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

Koh, P. W., Steinhardt, J., & Liang, P. (2018) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 189

DataComp: In search of the next generation of multimodal datasets

Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 120

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 74

Synthesizing scientific literature with retrieval-augmented language models

Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D’Arcy, M., et al. (2026) · Nature

Topic ModelingInformation Retrieval and Search Behavior

Cited by 38

MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning

Li, S., Balachandran, V., Feng, S., Ilgen, J., Pierson, E., Koh, P., & Tsvetkov, Y. (2024) · Advances in Neural Information Processing Systems 37

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 19

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

Scaling Retrieval-Based Language Models with a Trillion-Token Datastore

Shao, R., He, J., Asai, A., Shi, W., Dettmers, T., Min, S., Zettlemoyer, L., & Koh, P. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingInformation Retrieval and Search Behavior

Cited by 8

OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs

Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D'arcy, M., et al. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 7

Language models scale reliably with over-training and on downstream tasks

Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

2 OLMo 2 Furious

OLMo, T., Walsh, P., Soldaini, L., Groeneveld, D., Lo, K., Arora, S., Bhagia, A., Gu, Y., Huang, S., Jordan, M., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

DataDecide: How to Predict Best Pretraining Data with Small Experiments

Magnusson, I., Tai, N., Bogin, B., Heineman, D., Hwang, J. D., Soldaini, L., Bhagia, A., Liu, J., Groeneveld, D., Tafjord, O., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

Spurious Rewards: Rethinking Training Signals in RLVR

Shao, R., Li, S. S., Xin, R., Geng, S., Wang, Y., Oh, S., Du, S. S., Lambert, N., Min, S., Krishna, R., et al. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 1

JobBench: Aligning Agent Work With Human Will

Li, Y., Feng, Y., Xu, Z., Ma, Z., Zheng, K., Jiang, F., Sun, X., Shao, R., Chen, Z., Huang, Y., et al. (2026) · arXiv

Ethics and Social Impacts of AIAI and HR Technologies

Cited by 0