| 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 | 325 | 2019 |
| Dialogue natural language inference S Welleck, J Weston, A Szlam, K Cho ACL 2019, 2018 | 192 | 2018 |
| 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 | 123 | 2019 |
| Non-Monotonic Sequential Text Generation S Welleck, K Brantley, H Daumé III, K Cho International Conference on Machine Learning (ICML) 2019, 2019 | 103 | 2019 |
| 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 | 96 | 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 | 91 | 2021 |
| 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 | 48 | 2021 |
| Consistency of a Recurrent Language Model With Respect to Incomplete Decoding S Welleck, I Kulikov, J Kim, RY Pang, K Cho EMNLP 2020, 2020 | 41 | 2020 |
| 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 | 39 | 2021 |
| A generalized framework of sequence generation with application to undirected sequence models E Mansimov, A Wang, S Welleck, K Cho arXiv preprint arXiv:1905.12790, 2019 | 37 | 2019 |
| 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 | 36* | 2022 |
| Cold decoding: Energy-based constrained text generation with langevin dynamics L Qin, S Welleck, D Khashabi, Y Choi NeurIPS 2022, 2022 | 30 | 2022 |
| Self-refine: Iterative refinement with self-feedback A Madaan, N Tandon, P Gupta, S Hallinan, L Gao, S Wiegreffe, U Alon, ... arXiv preprint arXiv:2303.17651, 2023 | 26 | 2023 |
| 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 | 22 | 2022 |
| NaturalProofs: Mathematical Theorem Proving in Natural Language S Welleck, J Liu, RL Bras, H Hajishirzi, Y Choi, K Cho NeurIPS 2021 Datasets & Benchmarks, 2021 | 22 | 2021 |
| Saliency-based sequential image attention with multiset prediction S Welleck, J Mao, K Cho, Z Zhang NeurIPS 2017, 2017 | 21 | 2017 |
| 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 | 19 | 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 | 17 | 2022 |
| Loss Functions for Multiset Prediction S Welleck, Z Yao, Y Gai, J Mao, Z Zhang, K Cho NeurIPS 2018, 2018 | 17 | 2018 |
| Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts D Khashabi, S Lyu, S Min, L Qin, K Richardson, S Singh, S Welleck, ... NAACL 2022, 2021 | 15* | 2021 |
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
