| Language models are few-shot learners T Brown, B Mann, N Ryder, M Subbiah, JD Kaplan, P Dhariwal, ... NeurIPS, 2020 | 24578 | 2020 |
| Cord blood T cell subpopulations and associations with maternal cadmium and arsenic exposures UC Nygaard, Z Li, T Palys, B Jackson, M Subbiah, M Malipatlolla, ... PLoS One 12 (6), e0179606, 2017 | 35 | 2017 |
| Safetext: A benchmark for exploring physical safety in language models S Levy, E Allaway, M Subbiah, L Chilton, D Patton, K McKeown, ... EMNLP, 2022 | 17 | 2022 |
| Check-covid: Fact-checking COVID-19 news claims with scientific evidence G Wang, K Harwood, L Chillrud, A Ananthram, M Subbiah, K McKeown ACL Findings, 2023 | 6 | 2023 |
| Mitigating covertly unsafe text within natural language systems A Mei, A Kabir, S Levy, M Subbiah, E Allaway, J Judge, D Patton, ... EMNLP Findings, 2022 | 5 | 2022 |
| Training with simulated images MS Subbiah, JR Lesser, NE Apostoloff US Patent 11,256,958, 2022 | 5 | 2022 |
| Towards detecting harmful agendas in news articles M Subbiah, A Bhattacharjee, Y Hua, T Kumarage, H Liu, K McKeown WASSA Workshop at ACL, 2023 | 4 | 2023 |
| Unsupervised Selective Rationalization with Noise Injection A Storek, M Subbiah, K McKeown ACL, 2023 | 1 | 2023 |
| Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers M Subbiah, S Zhang, LB Chilton, K McKeown arXiv preprint arXiv:2403.01061, 2024 | | 2024 |
| Understanding Identity Signalling in Persuasive Online Text. M Subbiah, KR McKeown SocialSens Workshop at ICWSM, 2021 | | 2021 |
