| Self-refine: Iterative refinement with self-feedback A Madaan, N Tandon, P Gupta, S Hallinan, L Gao, S Wiegreffe, U Alon, ... NeurIPS 2023, 2023 | 1235 | 2023 |
| Neural text generation with unlikelihood training S Welleck, I Kulikov, S Roller, E Dinan, K Cho, J Weston International Conference on Learning Representations (ICLR) 2020, 2019 | 587 | 2019 |
| Faith and Fate: Limits of Transformers on Compositionality N Dziri, X Lu, M Sclar, XL Li, L Jian, BY Lin, P West, C Bhagavatula, ... NeurIPS 2023, 2023 | 332 | 2023 |
| Generated knowledge prompting for commonsense reasoning J Liu, A Liu, X Lu, S Welleck, P West, RL Bras, Y Choi, H Hajishirzi ACL 2022, 2021 | 332 | 2021 |
| Mauve: Measuring the gap between neural text and human text using divergence frontiers K Pillutla, S Swayamdipta, R Zellers, J Thickstun, S Welleck, Y Choi, ... Advances in Neural Information Processing Systems 34, 4816-4828, 2021 | 327 | 2021 |
| Symbolic knowledge distillation: from general language models to commonsense models P West, C Bhagavatula, J Hessel, JD Hwang, L Jiang, RL Bras, X Lu, ... NAACL 2022, 2021 | 317 | 2021 |
| Dialogue natural language inference S Welleck, J Weston, A Szlam, K Cho ACL 2019, 2018 | 306 | 2018 |
| Llemma: an open language model for mathematics Z Azerbayev, H Schoelkopf, K Paster, M Dos Santos, S McAleer, AQ Jiang, ... ICLR 2024, 2024 | 261 | 2024 |
| Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training M Li, S Roller, I Kulikov, S Welleck, YL Boureau, K Cho, J Weston ACL 2020, 2019 | 209 | 2019 |
| Quark: Controllable text generation with reinforced unlearning X Lu, S Welleck, L Jiang, J Hessel, L Qin, P West, P Ammanabrolu, Y Choi NeurIPS 2022, 2022 | 181 | 2022 |
| Maieutic prompting: Logically consistent reasoning with recursive explanations J Jung, L Qin, S Welleck, F Brahman, C Bhagavatula, RL Bras, Y Choi EMNLP 2022, 2022 | 181* | 2022 |
| Generating Sequences by Learning to Self-Correct S Welleck, X Lu, P West, F Brahman, T Shen, D Khashabi, Y Choi ICLR 2023, 2022 | 176* | 2022 |
| Neurologic a* esque decoding: Constrained text generation with lookahead heuristics X Lu, S Welleck, P West, L Jiang, J Kasai, D Khashabi, RL Bras, L Qin, ... NAACL 2022, 2021 | 162 | 2021 |
| Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs AQ Jiang, S Welleck, JP Zhou, W Li, J Liu, M Jamnik, T Lacroix, Y Wu, ... ICLR 2023, 2022 | 144 | 2022 |
| Cold decoding: Energy-based constrained text generation with langevin dynamics L Qin, S Welleck, D Khashabi, Y Choi NeurIPS 2022, 2022 | 138 | 2022 |
| Non-Monotonic Sequential Text Generation S Welleck, K Brantley, H Daumé III, K Cho International Conference on Machine Learning (ICML) 2019, 2019 | 132 | 2019 |
| A Survey of Deep Learning for Mathematical Reasoning P Lu, L Qiu, W Yu, S Welleck, KW Chang ACL 2023, 2022 | 129 | 2022 |
| Lila: A unified benchmark for mathematical reasoning S Mishra, M Finlayson, P Lu, L Tang, S Welleck, C Baral, T Rajpurohit, ... EMNLP 2022, 2022 | 116 | 2022 |
| Prometheus 2: An open source language model specialized in evaluating other language models S Kim, J Suk, S Longpre, BY Lin, J Shin, S Welleck, G Neubig, M Lee, ... EMNLP 2024, 2024 | 99 | 2024 |
| Consistency of a Recurrent Language Model With Respect to Incomplete Decoding S Welleck, I Kulikov, J Kim, RY Pang, K Cho EMNLP 2020, 2020 | 78 | 2020 |
Yejin ChoiStanford University / NVIDIAstanford.edu의 이메일 확인됨