Richard Yuanzhe Pang

According to our database, Richard Yuanzhe Pang authored at least 31 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Transformers Struggle to Learn to Search.

Abulhair Saparov, Srushti Pawar, Shreyas Pimpalgaonkar, Nitish Joshi, Richard Yuanzhe Pang, Vishakh Padmakumar, Seyed Mehran Kazemi, Najoung Kim, He He

CoRR, 2024

Self-Generated Critiques Boost Reward Modeling for Language Models.

Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou

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

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

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, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Xiaoqing Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari Hemmat, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra

CoRR, 2024

Iterative Reasoning Preference Optimization.

Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, Jason Weston

CoRR, 2024

Self-Rewarding Language Models.

Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, Jason Weston

CoRR, 2024

Self-Rewarding Language Models.

Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, Jason Weston

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Leveraging Implicit Feedback from Deployment Data in Dialogue.

Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho, He He, Jason Weston

Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2024

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

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Extrapolative Controlled Sequence Generation via Iterative Refinement.

Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P. Parikh

Proceedings of the International Conference on Machine Learning, 2023

Reward Gaming in Conditional Text Generation.

Richard Yuanzhe Pang, Vishakh Padmakumar, Thibault Sellam, Ankur P. Parikh, He He

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

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

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

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, Samuel R. Bowman

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

SQuALITY: Building a Long-Document Summarization Dataset the Hard Way.

Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Token Dropping for Efficient BERT Pretraining.

Le Hou, Richard Yuanzhe Pang, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, Denny Zhou

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Amortized Noisy Channel Neural Machine Translation.

Richard Yuanzhe Pang, He He, Kyunghyun Cho

CoRR, 2021

Text Generation by Learning from Demonstrations.

Richard Yuanzhe Pang, He He

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

AgreeSum: Agreement-Oriented Multi-Document Summarization.

Richard Yuanzhe Pang, Ádám Dániel Lelkes, Vinh Q. Tran, Cong Yu

Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020

Text Generation by Learning from Off-Policy Demonstrations.

Richard Yuanzhe Pang, He He

CoRR, 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

Consistency of a Recurrent Language Model With Respect to Incomplete Decoding.

Sean Welleck, Ilia Kulikov, Jaedeok Kim, Richard Yuanzhe Pang, Kyunghyun Cho

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation.

Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, Kevin Gimpel

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 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

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

Improving Joint Training of Inference Networks and Structured Prediction Energy Networks.

Lifu Tu, Richard Yuanzhe Pang, Kevin Gimpel

Proceedings of the Fourth Workshop on Structured Prediction for NLP@EMNLP 2020, 2020

2019

The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation.

Richard Yuanzhe Pang

CoRR, 2019

Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer.

Richard Yuanzhe Pang, Kevin Gimpel

Proceedings of the 3rd Workshop on Neural Generation and Translation@EMNLP-IJCNLP 2019, 2019

Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation.

Richard Yuanzhe Pang

Proceedings of the 5th Workshop on Noisy User-generated Text, 2019

2018

Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora.

Richard Yuanzhe Pang, Kevin Gimpel

CoRR, 2018