Welleck, Sean

Self-Refine: Iterative Refinement with Self-Feedback

Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,314

Neural Text Generation with Unlikelihood Training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 254

Dialogue Natural Language Inference

Welleck, S., Weston, J., Szlam, A., & Cho, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 249

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., & Choi, Y. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 98

Generating Sequences by Learning to Self-Correct

Welleck, S., Lu, X., West, P., Brahman, F., Shen, T., Khashabi, D., & Choi, Y. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 41

Lean-STaR: Learning to Interleave Thinking and Proving

Lin, H., Sun, Z., Welleck, S., & Yang, Y. (2024) · arXiv

Logic, programming, and type systemsMathematics, Computing, and Information Processing

Cited by 14

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Kim, S., Suk, J., Longpre, S., Lin, B. Y., Shin, J., Welleck, S., Neubig, G., Lee, M., Lee, K., & Seo, M. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement

Aggarwal, P., Parno, B., & Welleck, S. (2024) · arXiv

Logic, programming, and type systemsSoftware Engineering Research

Cited by 4

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Sun, Z., Yu, L., Shen, Y., Liu, W., Yang, Y., Welleck, S., & Gan, C. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

Welleck, S., Bertsch, A., Finlayson, M., Schoelkopf, H., Xie, A., Neubig, G., Kulikov, I., & Harchaoui, Z. (2024) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 3

miniCodeProps: a Minimal Benchmark for Proving Code Properties

Lohn, E., & Welleck, S. (2024) · arXiv

Logic, programming, and type systemsFormal Methods in Verification

Cited by 2

L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Aggarwal, P., & Welleck, S. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

Kim, S., Yoon, D., Gashteovski, K., Suk, J., Baek, J., Aggarwal, P., Wu, I., Zaverkin, V., Petkoski, S., Schrider, D. R., et al. (2026) · arXiv

Academic Writing and PublishingAcademic Publishing and Open Access

Cited by 1