Saliency-based Sequential Image Attention with Multiset Prediction. - Sean Welleck; Jialin Mao; Kyunghyun Cho; Zheng Zhang 001 (NIPS, 2017)

Loss Functions for Multiset Prediction. - Sean Welleck; Zixin Yao; Yu Gai; Jialin Mao; Zheng Zhang 001; Kyunghyun Cho (NeurIPS, 2018)

Dialogue Natural Language Inference. - Sean Welleck; Jason Weston; Arthur Szlam; Kyunghyun Cho (ACL (1), 2019)

Non-Monotonic Sequential Text Generation. - Sean Welleck; Kianté Brantley; Hal Daumé III; Kyunghyun Cho (ICML, 2019)

Sequential Graph Dependency Parser. - Sean Welleck; Kyunghyun Cho (RANLP, 2019)

Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training. - Margaret Li; Stephen Roller; Ilia Kulikov; Sean Welleck; Y-Lan Boureau; Kyunghyun Cho; Jason Weston (ACL, 2020)

Consistency of a Recurrent Language Model With Respect to Incomplete Decoding. - Sean Welleck; Ilia Kulikov; Jaedeok Kim; Richard Yuanzhe Pang; Kyunghyun Cho (EMNLP (1), 2020)

Neural Text Generation With Unlikelihood Training. - Sean Welleck; Ilia Kulikov; Stephen Roller; Emily Dinan; Kyunghyun Cho; Jason Weston (ICLR, 2020)

MLE-Guided Parameter Search for Task Loss Minimization in Neural Sequence Modeling. - Sean Welleck; Kyunghyun Cho (AAAI, 2021)

Mode recovery in neural autoregressive sequence modeling. - Ilia Kulikov; Sean Welleck; Kyunghyun Cho (SPNLP@ACL-IJCNLP, 2021)

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals. - Lang Liu; Krishna Pillutla; Sean Welleck; Sewoong Oh; Yejin Choi 001; Zaïd Harchaoui (NeurIPS, 2021)

MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. - Krishna Pillutla; Swabha Swayamdipta; Rowan Zellers; John Thickstun; Sean Welleck; Yejin Choi 001; Zaïd Harchaoui (NeurIPS, 2021)

NaturalProofs: Mathematical Theorem Proving in Natural Language. - Sean Welleck; Jiacheng Liu 010; Ronan Le Bras 001; Hanna Hajishirzi; Yejin Choi 001; Kyunghyun Cho (NeurIPS Datasets and Benchmarks, 2021)

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral. - Lang Liu; Krishna Pillutla; Sean Welleck; Sewoong Oh; Yejin Choi 001; Zaïd Harchaoui (CoRR, 2021)

PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts. - Daniel Khashabi; Shane Lyu; Sewon Min; Lianhui Qin; Kyle Richardson 001; Sameer Singh 001; Sean Welleck; Hannaneh Hajishirzi; Tushar Khot; Ashish Sabharwal; Yejin Choi 001 (CoRR, 2021)

Order and Learning in Sequential Neural Structured Prediction. - Sean Welleck (2021)

Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics. - Sean Welleck; Peter West; Jize Cao; Yejin Choi 001 (AAAI, 2022)

Generated Knowledge Prompting for Commonsense Reasoning. - Jiacheng Liu 010; Alisa Liu; Ximing Lu; Sean Welleck; Peter West; Ronan Le Bras 001; Yejin Choi 001; Hannaneh Hajishirzi (ACL (1), 2022)

Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering. - Jiacheng Liu 010; Skyler Hallinan; Ximing Lu; Pengfei He; Sean Welleck; Hannaneh Hajishirzi; Yejin Choi 001 (EMNLP, 2022)

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations. - Jaehun Jung; Lianhui Qin; Sean Welleck; Faeze Brahman; Chandra Bhagavatula; Ronan Le Bras 001; Yejin Choi 001 (EMNLP, 2022)

LILA: A Unified Benchmark for Mathematical Reasoning. - Swaroop Mishra; Matthew Finlayson; Pan Lu; Leonard Tang; Sean Welleck; Chitta Baral; Tanmay Rajpurohit; Oyvind Tafjord; Ashish Sabharwal; Peter Clark; Ashwin Kalyan (EMNLP, 2022)

Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. - Daniel Khashabi; Xinxi Lyu; Sewon Min; Lianhui Qin; Kyle Richardson 001; Sean Welleck; Hannaneh Hajishirzi; Tushar Khot; Ashish Sabharwal; Sameer Singh 001; Yejin Choi 001 (NAACL-HLT, 2022)

NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics. - Ximing Lu; Sean Welleck; Peter West; Liwei Jiang; Jungo Kasai; Daniel Khashabi; Ronan Le Bras 001; Lianhui Qin; Youngjae Yu; Rowan Zellers; Noah A. Smith; Yejin Choi 001 (NAACL-HLT, 2022)

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models. - Peter West; Chandra Bhagavatula; Jack Hessel; Jena D. Hwang; Liwei Jiang; Ronan Le Bras 001; Ximing Lu; Sean Welleck; Yejin Choi 001 (NAACL-HLT, 2022)

QUARK: Controllable Text Generation with Reinforced Unlearning. - Ximing Lu; Sean Welleck; Jack Hessel; Liwei Jiang; Lianhui Qin; Peter West; Prithviraj Ammanabrolu; Yejin Choi 001 (NeurIPS, 2022)

COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics. - Lianhui Qin; Sean Welleck; Daniel Khashabi; Yejin Choi 001 (NeurIPS, 2022)

NaturalProver: Grounded Mathematical Proof Generation with Language Models. - Sean Welleck; Jiacheng Liu 010; Ximing Lu; Hannaneh Hajishirzi; Yejin Choi 001 (NeurIPS, 2022)

A Survey of Deep Learning for Mathematical Reasoning. - Pan Lu; Liang Qiu 001; Wenhao Yu 002; Sean Welleck; Kai-Wei Chang (ACL (1), 2023)

STEER: Unified Style Transfer with Expert Reinforcement. - Skyler Hallinan; Faeze Brahman; Ximing Lu; Jaehun Jung; Sean Welleck; Yejin Choi 001 (EMNLP (Findings), 2023)

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning. - Ximing Lu; Faeze Brahman; Peter West; Jaehun Jung; Khyathi Raghavi Chandu; Abhilasha Ravichander; Prithviraj Ammanabrolu; Liwei Jiang; Sahana Ramnath; Nouha Dziri; Jillian Fisher; Bill Y. Lin; et al. (EMNLP, 2023)

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. - Albert Qiaochu Jiang; Sean Welleck; Jin Peng Zhou; Timothée Lacroix; Jiacheng Liu 010; Wenda Li; Mateja Jamnik; Guillaume Lample; Yuhuai Wu (ICLR, 2023)

Generating Sequences by Learning to Self-Correct. - Sean Welleck; Ximing Lu; Peter West; Faeze Brahman; Tianxiao Shen; Daniel Khashabi; Yejin Choi 001 (ICLR, 2023)

Faith and Fate: Limits of Transformers on Compositionality. - Nouha Dziri; Ximing Lu; Melanie Sclar; Xiang Lorraine Li; Liwei Jiang; Bill Yuchen Lin; Sean Welleck; Peter West; Chandra Bhagavatula; Ronan Le Bras 001; Jena D. Hwang; Soumya Sanyal 001; et al. (NeurIPS, 2023)

Self-Refine: Iterative Refinement with Self-Feedback. - Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon 002; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; et al. (NeurIPS, 2023)

Self-Refine: Iterative Refinement with Self-Feedback. - Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon 002; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Sean Welleck; Bodhisattwa Prasad Majumder; et al. (CoRR, 2023)

LLMSTEP: LLM proofstep suggestions in Lean. - Sean Welleck; Rahul Saha (CoRR, 2023)

MAUVE Scores for Generative Models: Theory and Practice. - Krishna Pillutla; Lang Liu; John Thickstun; Sean Welleck; Swabha Swayamdipta; Rowan Zellers; Sewoong Oh; Yejin Choi 001; Zaïd Harchaoui (J. Mach. Learn. Res., 2023)

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models. - Seungone Kim; Juyoung Suk; Shayne Longpre; Bill Yuchen Lin; Jamin Shin; Sean Welleck; Graham Neubig; Moontae Lee; Kyungjae Lee 002; Minjoon Seo (EMNLP, 2024)

Llemma: An Open Language Model for Mathematics. - Zhangir Azerbayev; Hailey Schoelkopf; Keiran Paster; Marco Dos Santos; Stephen Marcus McAleer; Albert Q. Jiang; Jia Deng; Stella Biderman; Sean Welleck (ICLR, 2024)

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision. - Zhiqing Sun; Longhui Yu; Yikang Shen; Weiyang Liu; Yiming Yang; Sean Welleck; Chuang Gan 001 (NeurIPS, 2024)

miniCodeProps: a Minimal Benchmark for Proving Code Properties. - Evan Lohn; Sean Welleck (CoRR, 2024)

An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models. - Yangzhen Wu; Zhiqing Sun; Shanda Li; Sean Welleck; Yiming Yang (CoRR, 2024)

AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement. - Pranjal Aggarwal; Bryan Parno; Sean Welleck (CoRR, 2024)

Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning. - Simon Frieder; Jonas Bayer; Katherine M. Collins; Julius Berner; Jacob Loader; András Juhász; Fabian Ruehle; Sean Welleck; Gabriel Poesia; Ryan-Rhys Griffiths; Adrian Weller; Anirudh Goyal; et al. (CoRR, 2024)

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models. - Sean Welleck; Amanda Bertsch; Matthew Finlayson; Hailey Schoelkopf; Alex Xie; Graham Neubig; Ilia Kulikov; Zaïd Harchaoui (Trans. Mach. Learn. Res., 2024)

Evaluating Language Models as Synthetic Data Generators. - Seungone Kim; Juyoung Suk; Xiang Yue; Vijay Viswanathan 002; Seongyun Lee; Yizhong Wang; Kiril Gashteovski; Carolin Lawrence; Sean Welleck; Graham Neubig (ACL (1), 2025)

ImProver: Agent-Based Automated Proof Optimization. - Riyaz Ahuja; Jeremy Avigad; Prasad Tetali; Sean Welleck (ICLR, 2025)

miniCTX: Neural Theorem Proving with (Long-)Contexts. - Jiewen Hu; Thomas Zhu; Sean Welleck (ICLR, 2025)

Lean-STaR: Learning to Interleave Thinking and Proving. - Haohan Lin; Zhiqing Sun; Sean Welleck; Yiming Yang (ICLR, 2025)

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving. - Yangzhen Wu; Zhiqing Sun; Shanda Li; Sean Welleck; Yiming Yang (ICLR, 2025)

The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models. - Seungone Kim; Juyoung Suk; Ji Yong Cho; Shayne Longpre; Chaeeun Kim; Dongkeun Yoon; Guijin Son; Yejin Choi 001; Sheikh Shafayat; Jinheon Baek; Sue Hyun Park; Hyeonbin Hwang; et al. (NAACL (Long Papers), 2025)

Optimizing Temperature for Language Models with Multi-Sample Inference. - Weihua Du; Yiming Yang; Sean Welleck (CoRR, 2025)

Programming with Pixels: Computer-Use Meets Software Engineering. - Pranjal Aggarwal; Sean Welleck (CoRR, 2025)

L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning. - Pranjal Aggarwal; Sean Welleck (CoRR, 2025)

Scaling Evaluation-time Compute with Reasoning Models as Process Evaluators. - Seungone Kim; Ian Wu; Jinu Lee; Xiang Yue; Seongyun Lee; Mingyeong Moon; Kiril Gashteovski; Carolin Lawrence; Julia Hockenmaier; Graham Neubig; Sean Welleck (CoRR, 2025)

The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think. - Seongyun Lee; Seungone Kim; Minju Seo; Yongrae Jo; Dongyoung Go; Hyeonbin Hwang; Jinho Park 005; Xiang Yue; Sean Welleck; Graham Neubig; Moontae Lee; Minjoon Seo (CoRR, 2025)

Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening. - Andre He; Daniel Fried; Sean Welleck (CoRR, 2025)

Premise Selection for a Lean Hammer. - Thomas Zhu; Joshua Clune; Jeremy Avigad; Albert Qiaochu Jiang; Sean Welleck (CoRR, 2025)

Agentic-R1: Distilled Dual-Strategy Reasoning. - Weihua Du; Pranjal Aggarwal; Sean Welleck; Yiming Yang (CoRR, 2025)

OptimalThinkingBench: Evaluating Over and Underthinking in LLMs. - Pranjal Aggarwal; Seungone Kim; Jack Lanchantin; Sean Welleck; Jason Weston; Ilia Kulikov; Swarnadeep Saha (CoRR, 2025)