Gao, Leo

Multitask Prompted Training Enables Zero-Shot Task Generalization

Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 612

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 492

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 67

Scaling Laws for Reward Model Overoptimization

Gao, L., Schulman, J., & Hilton, J. (2022) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 62

Collaborative Storytelling with Large-scale Neural Language Models

Nichols, E., Gao, L., & Gomez, R. (2020) · Motion, Interaction and Games

Artificial Intelligence in GamesTopic Modeling

Cited by 50

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Burns, C., Izmailov, P., Kirchner, J. H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 34

Scaling and evaluating sparse autoencoders

Gao, L., la Tour, T. D., Tillman, H., Goh, G., Troll, R., Radford, A., Sutskever, I., Leike, J., & Wu, J. (2024) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 21

Datasheet for the Pile

Biderman, S., Bicheno, K., & Gao, L. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

Lessons from the Trenches on Reproducible Evaluation of Language Models

Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation

Baker, B., Huizinga, J., Gao, L., Dou, Z., Guan, M. Y., Madry, A., Zaremba, W., Pachocki, J., & Farhi, D. (2025) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 8

EleutherAI: Going Beyond "Open Science" to "Science in the Open"

Phang, J., Bradley, H., Gao, L., Castricato, L., & Biderman, S. (2022) · arXiv

Academic Publishing and Open AccessOpen Source Software Innovations

Cited by 3