Phang, Jason

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

BBQ: A Hand-Built Bias Benchmark for Question Answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. R. (2021) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 189

BBQ: A hand-built bias benchmark for question answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Ethics and Social Impacts of AITopic Modeling

Cited by 186

Tool Learning with Foundation Models

Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., et al. (2023) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 184

GPT-4o System Card

OpenAI, :, Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 180

How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study

Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., Maes, P., Phang, J., Lampe, M., Ahmad, L., et al. (2025) · arXiv

Social Robot Interaction and HRIDigital Mental Health Interventions

Cited by 109

How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study

Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., Maes, P., Phang, J., Lampe, M., Ahmad, L., et al. (2026) · Research Square

Social Robot Interaction and HRIDigital Mental Health Interventions

Cited by 109

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

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

What Language Model to Train if You Have One Million GPU Hours?

Le Scao, T., Wang, T., Hesslow, D., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., Press, O., et al. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingNatural Language Processing Techniques

Cited by 33

Investigating Affective Use and Emotional Well-being on ChatGPT

Phang, J., Lampe, M., Ahmad, L., Agarwal, S., Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., et al. (2025) · arXiv

Innovative Human-Technology InteractionSocial Robot Interaction and HRI

Cited by 29

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

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

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

What Language Model to Train if You Have One Million GPU Hours?

Scao, T. L., Wang, T., Hesslow, D., Saulnier, L., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

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

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