publications

2024

  1. Advancing LLM Reasoning Generalists with Preference Trees
    Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, and 5 more authors
    arXiv preprint, 2024
    paper code
  2. Source-Aware Training Enables Knowledge Attribution in Language Models
    Muhammad Khalifa, David Wadden, Emma Strubell, Honglak Lee, Lu Wang, Iz Beltagy, and Hao Peng
    arXiv preprint, 2024
    paper
  3. Language Models Hallucinate, but May Excel at Fact Verification
    Jian Guan, Jesse Dodge, David Wadden, Minlie Huang, and Hao Peng
    In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024
    paper code
  4. LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models
    Chi Han, Qifan Wang, Hao Peng, Wenhan Xiong, Yu Chen, Heng Ji, and Sinong Wang
    In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024
    paper code
  5. Executable Code Actions Elicit Better LLM Agents
    Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji
    arXiv preprint, 2024
    paper website code
  6. Data Engineering for Scaling Language Models to 128K Context
    Yao Fu, Rameswar Panda, Xinyao Niu, Xiang Yue, Hannaneh Hajishirzi, Yoon Kim, and Hao Peng
    arXiv preprint, 2024
    paper code
  7. Examining LLMs’ Uncertainty Expression Towards Questions Outside Parametric Knowledge
    Genglin Liu, Xingyao Wang, Lifan Yuan, Yangyi Chen, and Hao Peng
    arXiv preprint, 2024
  8. spotlight
    TRAM: Bridging Trust Regions and Sharpness Aware Minimization
    Tom Sherborne, Naomi Saphra, Pradeep Dasigi, and Hao Peng
    In Proceedings of the International Conference on Learning Representations (ICLR), 2024
    paper code
  9. MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
    Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, and Heng Ji
    In Proceedings of the International Conference on Learning Representations (ICLR), 2024
    paper website code data
  10. CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
    Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi R. Fung, Hao Peng, and Heng Ji
    In Proceedings of the International Conference on Learning Representations (ICLR), 2024
    paper code
  11. LeTI: Learning to Generate from Textual Interactions
    Xingyao Wang, Hao Peng, Reyhaneh Jabbarvand, and Heng Ji
    In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024
    paper code

2023

  1. FiLM: Fill-in Language Models for Any-Order Generation
    Tianxiao Shen, Hao Peng, Ruoqi Shen, Yao Fu, Zaid Harchaoui, and Yejin Choi
    arXiv preprint, 2023
    paper code
  2. Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
    Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata
    arXiv preprint, 2023
    paper code
  3. Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
    Hao Peng, Qingqing Cao, Jesse Dodge, Matthew E. Peters, Jared Fernandez, Tom Sherborne, Kyle Lo, Sam Skjonsberg, Emma Strubell, Darrell Plessas, and 4 more authors
    arXiv preprint, 2023
    paper code
  4. Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models’ Reasoning Performance
    Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, and Tushar Khot
    arXiv preprint, 2023
    paper website
  5. oral
    Specializing Smaller Language Models towards Multi-Step Reasoning
    Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot
    In Proceedings of the International Conference on Machine Learning (ICML), 2023
    paper code data
  6. Complexity-Based Prompting for Multi-step Reasoning
    Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot
    In Proceedings of the International Conference on Learning Representations (ICLR), 2023
    paper code
  7. Transparency Helps Reveal When Language Models Learn Meaning
    Zhaofeng Wu, William Merrill, Hao Peng, Iz Beltagy, and Noah A. Smith
    Transactions of the Association for Computational Linguistics (TACL), 2023
    paper code poster slides

2022

  1. How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers
    Michael Hassid, Hao Peng, Daniel Rotem, Jungo Kasai, Ivan Montero, Noah A. Smith, and Roy Schwartz
    In Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
    paper
  2. Modeling Context With Linear Attention for Scalable Document-Level Translation
    Zhaofeng Wu, Hao Peng, Nikolaos Pappas, and Noah A. Smith
    In Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
    paper
  3. Twist Decoding: Diverse Generators Guide Each Other
    Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, and Noah A. Smith
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
    paper
  4. ABC: Attention with Bounded-memory Control
    Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, and Noah A. Smith
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2022
    paper
  5. Tailor: Generating and Perturbing Text with Semantic Controls
    Alexis Ross, Tongshuang Wu, Hao Peng, Matthew Peters, and Matt Gardner
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2022
    paper

2021

  1. Finetuning Pretrained Transformers into RNNs
    Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, and Noah A. Smith
    In In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
    paper
  2. spotlight
    Random Feature Attention
    Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah Smith, and Lingpeng Kong
    In Proceedings of the International Conference on Learning Representations (ICLR), 2021
    paper
  3. Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
    Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, and Noah Smith
    In Proceedings of the International Conference on Learning Representations (ICLR), 2021
    paper
  4. Contextualized Perturbation for Textual Adversarial Attack
    Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, and Bill Dolan
    In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021
  5. Infusing Finetuning with Semantic Dependencies
    Zhaofeng Wu, Hao Peng, and Noah A. Smith
    Transactions of the Association for Computational Linguistics (TACL), 2021
    paper

2020

  1. A Mixture of h - 1 Heads is Better than h Heads
    Hao Peng, Roy Schwartz, Dianqi Li, and Noah A. Smith
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2020
    paper

2019

  1. PaLM: A Hybrid Parser and Language Model
    Hao Peng, Roy Schwartz, and Noah A. Smith
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
    paper
  2. RNN Architecture Learning with Sparse Regularization
    Jesse Dodge, Roy Schwartz, Hao Peng, and Noah A. Smith
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
    paper
  3. Text Generation with Exemplar-based Adaptive Decoding
    Hao Peng, Ankur Parikh, Manaal Faruqui, Bhuwan Dhingra, and Dipanjan Das
    In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019
    paper

2018

  1. Rational Recurrences
    Hao Peng, Roy Schwartz, Sam Thomson, and Noah A. Smith
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018
    paper
  2. best paper
    honorable mention
    Backpropagating through Structured Argmax using a SPIGOT
    Hao Peng, Sam Thomson, and Noah A. Smith
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2018
    paper
  3. Learning Joint Semantic Parsers from Disjoint Data
    Hao Peng, Sam Thomson, Swabha Swayamdipta, and Noah A. Smith
    In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018
    paper
  4. "You Are No Jack Kennedy": On Media Selection of Highlights from Presidential Debates
    Chenhao Tan, Hao Peng, and Noah A. Smith
    In Proceedings of The Web Conference (WWW), 2018
    paper

2017

  1. Deep Multitask Learning for Semantic Dependency Parsing
    Hao Peng, Sam Thomson, and Noah A. Smith
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2017
    paper

2016

  1. A Convolutional Attention Network for Extreme Summarization of Source Code
    Miltiadis Allamanis, Hao Peng, and Charles Sutton
    In Proceedings of the International Conference on Machine Learning (ICML), 2016
    paper

2015

  1. Discriminative Neural Sentence Modeling by Tree-Based Convolution
    Lili Mou, Hao Peng, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015
    paper
  2. Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
    Yan Xu, Lili Mou, Ge Li, Yunchuan Chen, Hao Peng, and Zhi Jin
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015
    paper