| Direct preference optimization: Your language model is secretly a reward model R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn Neural Information Processing Systems (NeurIPS), 2023 | 1886 | 2023 |
| Detectgpt: Zero-shot machine-generated text detection using probability curvature E Mitchell, Y Lee, A Khazatsky, CD Manning, C Finn International Conference on Machine Learning (ICML), 2023 | 494 | 2023 |
| Fast model editing at scale E Mitchell, C Lin, A Bosselut, C Finn, CD Manning International Conference on Learning Representations (ICLR), 2021 | 415 | 2021 |
| Memory-Based Model Editing at Scale E Mitchell, C Lin, A Bosselut, CD Manning, C Finn International Conference on Machine Learning (ICML), 2022 | 281 | 2022 |
| FlyWire: online community for whole-brain connectomics S Dorkenwald, CE McKellar, T Macrina, N Kemnitz, K Lee, R Lu, J Wu, ... Nature methods 19 (1), 119-128, 2022 | 206 | 2022 |
| Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback K Tian, E Mitchell, A Zhou, A Sharma, R Rafailov, H Yao, C Finn, ... Empirical Methods in Natural Language Processing (EMNLP), 2023 | 191 | 2023 |
| Learning language-conditioned robot behavior from offline data and crowd-sourced annotation S Nair, E Mitchell, K Chen, S Savarese, C Finn Conference on Robot Learning, 1303-1315, 2022 | 152 | 2022 |
| Functional connectomics spanning multiple areas of mouse visual cortex MICrONS Consortium, JA Bae, M Baptiste, CA Bishop, AL Bodor, ... BioRxiv, 2021.07. 28.454025, 2021 | 151 | 2021 |
| Neuronal wiring diagram of an adult brain S Dorkenwald, A Matsliah, AR Sterling, P Schlegel, SC Yu, CE McKellar, ... Nature 634 (8032), 124-138, 2024 | 141 | 2024 |
| Offline Meta-Reinforcement Learning with Advantage Weighting E Mitchell, R Rafailov, XB Peng, S Levine, C Finn International Conference on Machine Learning (ICML), 2021 | 114 | 2021 |
| Fine-tuning language models for factuality K Tian, E Mitchell, H Yao, CD Manning, C Finn arXiv preprint arXiv:2311.08401, 2023 | 110 | 2023 |
| On the opportunities and risks of foundation models. arXiv 2021 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2023 | 96 | 2023 |
| Identifying and mitigating the security risks of generative ai C Barrett, B Boyd, E Bursztein, N Carlini, B Chen, J Choi, AR Chowdhury, ... Foundations and TrendsŪ in Privacy and Security 6 (1), 1-52, 2023 | 74 | 2023 |
| On the opportunities and risks of foundation models (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2022 | 63 | 2022 |
| Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference E Mitchell, JJ Noh, S Li, WS Armstrong, A Agarwal, P Liu, C Finn, ... Empirical Methods in Natural Language Processing (EMNLP), 2022 | 45 | 2022 |
| Self-destructing models: Increasing the costs of harmful dual uses of foundation models P Henderson, E Mitchell, C Manning, D Jurafsky, C Finn Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 287-296, 2023 | 39 | 2023 |
| Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex CM Schneider-Mizell, AL Bodor, D Brittain, JA Buchanan, DJ Bumbarger, ... bioRxiv, 2023.01. 23.525290, 2024 | 38 | 2024 |
| Petascale neural circuit reconstruction: automated methods T Macrina, K Lee, R Lu, NL Turner, J Wu, S Popovych, W Silversmith, ... bioRxiv, 2021.08. 04.455162, 2021 | 35 | 2021 |
| Q-Learning for Continuous Actions with Cross-Entropy Guided Policies R Simmons-Edler, B Eisner, E Mitchell, S Seung, D Lee RL for Real Life Workshop, International Conference on Machine Learning, 2019 | 32 | 2019 |
| An emulator for fine-tuning large language models using small language models E Mitchell, R Rafailov, A Sharma, C Finn, CD Manning arXiv preprint arXiv:2310.12962, 2023 | 30 | 2023 |
Chelsea FinnStanford University, Physical IntelligenceVerified email at cs.stanford.edu
