Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora. - Richard Yuanzhe Pang; Kevin Gimpel (CoRR, 2018)

Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation. - Richard Yuanzhe Pang (W-NUT@EMNLP, 2019)

Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer. - Richard Yuanzhe Pang; Kevin Gimpel (NGT@EMNLP-IJCNLP, 2019)

The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation. - Richard Yuanzhe Pang (CoRR, 2019)

Improving Joint Training of Inference Networks and Structured Prediction Energy Networks. - Lifu Tu; Richard Yuanzhe Pang; Kevin Gimpel (SPNLP@EMNLP, 2020)

Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? - Yada Pruksachatkun; Jason Phang; Haokun Liu; Phu Mon Htut; Xiaoyi Zhang; Richard Yuanzhe Pang; Clara Vania; Katharina Kann; Samuel R. Bowman (ACL, 2020)

ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation. - Lifu Tu; Richard Yuanzhe Pang; Sam Wiseman; Kevin Gimpel (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)

Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work? - Yada Pruksachatkun; Jason Phang; Haokun Liu; Phu Mon Htut; Xiaoyi Zhang; Richard Yuanzhe Pang; Clara Vania; Katharina Kann; Samuel R. Bowman (CoRR, 2020)

Text Generation by Learning from Off-Policy Demonstrations. - Richard Yuanzhe Pang; He He 001 (CoRR, 2020)

AgreeSum: Agreement-Oriented Multi-Document Summarization. - Richard Yuanzhe Pang; Ádám Dániel Lelkes; Vinh Q. Tran 002; Cong Yu 001 (ACL/IJCNLP (Findings), 2021)

Comparing Test Sets with Item Response Theory. - Clara Vania; Phu Mon Htut; William Huang; Dhara A. Mungra; Richard Yuanzhe Pang; Jason Phang; Haokun Liu; Kyunghyun Cho; Samuel R. Bowman (ACL/IJCNLP (1), 2021)

Text Generation by Learning from Demonstrations. - Richard Yuanzhe Pang; He He 001 (ICLR, 2021)

Amortized Noisy Channel Neural Machine Translation. - Richard Yuanzhe Pang; He He 001; Kyunghyun Cho (CoRR, 2021)

Token Dropping for Efficient BERT Pretraining. - Le Hou; Richard Yuanzhe Pang; Tianyi Zhou 001; Yuexin Wu; Xinying Song; Xiaodan Song; Denny Zhou (ACL (1), 2022)

SQuALITY: Building a Long-Document Summarization Dataset the Hard Way. - Alex Wang; Richard Yuanzhe Pang; Angelica Chen; Jason Phang; Samuel R. Bowman (EMNLP, 2022)

QuALITY: Question Answering with Long Input Texts, Yes! - Richard Yuanzhe Pang; Alicia Parrish; Nitish Joshi; Nikita Nangia; Jason Phang; Angelica Chen; Vishakh Padmakumar; Johnny Ma; Jana Thompson; He He 001; Samuel R. Bowman (NAACL-HLT, 2022)

What Do NLP Researchers Believe? Results of the NLP Community Metasurvey. - Julian Michael; Ari Holtzman; Alicia Parrish; Aaron Mueller; Alex Wang; Angelica Chen; Divyam Madaan; Nikita Nangia; Richard Yuanzhe Pang; Jason Phang; Samuel R. Bowman (ACL (1), 2023)

Reward Gaming in Conditional Text Generation. - Richard Yuanzhe Pang; Vishakh Padmakumar; Thibault Sellam; Ankur P. Parikh; He He 001 (ACL (1), 2023)

Extrapolative Controlled Sequence Generation via Iterative Refinement. - Vishakh Padmakumar; Richard Yuanzhe Pang; He He 001; Ankur P. Parikh (ICML, 2023)

Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. - Abulhair Saparov; Richard Yuanzhe Pang; Vishakh Padmakumar; Nitish Joshi; Mehran Kazemi; Najoung Kim; He He 001 (NeurIPS, 2023)

GPQA: A Graduate-Level Google-Proof Q&A Benchmark. - David Rein; Betty Li Hou; Asa Cooper Stickland; Jackson Petty; Richard Yuanzhe Pang; Julien Dirani; Julian Michael; Samuel R. Bowman (CoRR, 2023)

Leveraging Implicit Feedback from Deployment Data in Dialogue. - Richard Yuanzhe Pang; Stephen Roller; Kyunghyun Cho; He He 001; Jason Weston (EACL (2), 2024)

Self-Rewarding Language Models. - Weizhe Yuan; Richard Yuanzhe Pang; Kyunghyun Cho; Xian Li 003; Sainbayar Sukhbaatar; Jing Xu; Jason Weston (ICML, 2024)

Iterative Reasoning Preference Optimization. - Richard Yuanzhe Pang; Weizhe Yuan; He He 001; Kyunghyun Cho; Sainbayar Sukhbaatar; Jason Weston (NeurIPS, 2024)

Self-Rewarding Language Models. - Weizhe Yuan; Richard Yuanzhe Pang; Kyunghyun Cho; Sainbayar Sukhbaatar; Jing Xu; Jason Weston (CoRR, 2024)

An Introduction to Vision-Language Modeling. - Florian Bordes; Richard Yuanzhe Pang; Anurag Ajay; Alexander C. Li; Adrien Bardes; Suzanne Petryk; Oscar Mañas; Zhiqiu Lin; Anas Mahmoud 002; Bargav Jayaraman; Mark Ibrahim; Melissa Hall; et al. (CoRR, 2024)

Self-Taught Evaluators. - Tianlu Wang; Ilia Kulikov; Olga Golovneva; Ping Yu; Weizhe Yuan; Jane Dwivedi-Yu; Richard Yuanzhe Pang; Maryam Fazel-Zarandi; Jason Weston; Xian Li 003 (CoRR, 2024)

Self-Consistency Preference Optimization. - Archiki Prasad; Weizhe Yuan; Richard Yuanzhe Pang; Jing Xu; Maryam Fazel-Zarandi; Mohit Bansal; Sainbayar Sukhbaatar; Jason Weston; Jane A. Yu (CoRR, 2024)

Learning from Rewards in Text Generation - Richard Yuanzhe Pang (2024)

Transformers Struggle to Learn to Search. - Abulhair Saparov; Srushti Ajay Pawar; Shreyas Pimpalgaonkar; Nitish Joshi; Richard Yuanzhe Pang; Vishakh Padmakumar; Mehran Kazemi; Najoung Kim; He He 001 (ICLR, 2025)

Self-Generated Critiques Boost Reward Modeling for Language Models. - Yue Yu; Zhengxing Chen; Aston Zhang; Liang Tan; Chenguang Zhu 001; Richard Yuanzhe Pang; Yundi Qian; Xuewei Wang; Suchin Gururangan; Chao Zhang; Melanie Kambadur; Dhruv Mahajan 001; et al. (NAACL (Long Papers), 2025)