| The false promise of imitating proprietary llms A Gudibande, E Wallace, C Snell, X Geng, H Liu, P Abbeel, S Levine, ... arXiv preprint arXiv:2305.15717, 2023 | 149 | 2023 |
| Offline rl for natural language generation with implicit language q learning C Snell, I Kostrikov, Y Su, M Yang, S Levine arXiv preprint arXiv:2206.11871, 2022 | 78 | 2022 |
| Describing differences between text distributions with natural language R Zhong, C Snell, D Klein, J Steinhardt International Conference on Machine Learning, 27099-27116, 2022 | 36 | 2022 |
| Learning by distilling context C Snell, D Klein, R Zhong arXiv preprint arXiv:2209.15189, 2022 | 34 | 2022 |
| Approximating how single head attention learns C Snell, R Zhong, D Klein, J Steinhardt arXiv preprint arXiv:2103.07601, 2021 | 33 | 2021 |
| Scaling llm test-time compute optimally can be more effective than scaling model parameters C Snell, J Lee, K Xu, A Kumar arXiv preprint arXiv:2408.03314, 2024 | 31 | 2024 |
| Context-aware language modeling for goal-oriented dialogue systems C Snell, M Yang, J Fu, Y Su, S Levine arXiv preprint arXiv:2204.10198, 2022 | 23 | 2022 |
| Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models M Abdulhai, I White, C Snell, C Sun, J Hong, Y Zhai, K Xu, S Levine arXiv preprint arXiv:2311.18232, 2023 | 15 | 2023 |
| Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024 C Snell, J Lee, K Xu, A Kumar URL https://arxiv. org/abs/2408.03314 11, 0 | 9 | |
| The false promise of imitating proprietary llms (2023) A Gudibande, E Wallace, C Snell, X Geng, H Liu, P Abbeel, S Levine, ... arXiv preprint arXiv:2305.15717, 2023 | 7 | 2023 |
| The false promise of imitating proprietary language models A Gudibande, E Wallace, CV Snell, X Geng, H Liu, P Abbeel, S Levine, ... The Twelfth International Conference on Learning Representations, 2024 | 5 | 2024 |
| Non-programmers can label programs indirectly via active examples: A case study with text-to-SQL R Zhong, C Snell, D Klein, J Eisner Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023 | 5 | 2023 |
| Active programming by example with a natural language prior R Zhong, C Snell, D Klein, J Eisner arXiv preprint arXiv:2205.12422, 2022 | 3 | 2022 |
| Predicting emergent capabilities by finetuning CV Snell, E Wallace, D Klein, S Levine First Conference on Language Modeling, 2024 | 1 | 2024 |
| The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification? E Kosoy, M Belyi, CV Snell, B Lake, J Tenenbaum, A Gopnik Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43), 2021 | 1 | 2021 |
| Crop Stage Estimation: A Multi-Satellite Historical Model and a Scalable Neural Network Forecaster N Padmanabhan, A Mahesh, A Sripathy, A Sujithkumar, A Sun, C Snell, ... AGU Fall Meeting Abstracts 2020, IN011-08, 2020 | | 2020 |
