Preprints

• TreeRare: Syntax Tree-Guided Retrieval and Reasoning for Knowledge-Intensive Question Answering
  Boyi Zhang, Zhuo Liu, and Hangfeng He
  In arXiv 2025. [pdf]

• An Empirical Analysis of LLMs for Countering Misinformation
  Adiba Mahbub Proma, Neeley Pate, James Druckman, Gourab Ghoshal, Hangfeng He, and Ehsan Hoque
  In arXiv 2025. [pdf]

• On the Role of Model Prior in Real-World Inductive Reasoning
  Zhuo Liu, Ding Yu, and Hangfeng He
  In arXiv 2024. [pdf]

• MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models
  Hang Hua, Yunlong Tang, Ziyun Zeng, Liangliang Cao, Zhengyuan Yang, Hangfeng He, Chenliang Xu, and Jiebo Luo
  In arXiv 2024. [pdf]

• A Law of Next-Token Prediction in Large Language Models
  Hangfeng He and Weijie Su
  In arXiv 2024. [pdf] [code]

• Rethinking with Retrieval: Faithful Large Language Model Inference
  Hangfeng He, Hongming Zhang, and Dan Roth
  In arXiv 2023. [pdf] [code]

Publications

2025

• Same Company, Same Signal: The Role of Identity in Earnings Call Transcripts
  Ding Yu, Zhuo Liu, and Hangfeng He
  In ACL 2025 (findings). [pdf]

• Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting
  Jiarui Wu, Zhuo Liu, and Hangfeng He
  In NAACL 2025 (short papers, findings). [pdf]

2024

• An Empirical Analysis on Large Language Models in Debate Evaluation
  Xinyi Liu, Pinxin Liu, and Hangfeng He
  In ACL 2024 (short papers). [pdf]

• SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluation
  Hangfeng He, Hongming Zhang, and Dan Roth
  In NAACL 2024 (findings). [pdf] [code]

• Unveiling Divergent Inductive Biases of LLMs on Temporal Data
  Sindhu Kishore and Hangfeng He
  In NAACL 2024 (short papers). [pdf]

• Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
  Matteo Sordello, Niccolo Dalmasso, Hangfeng He, and Weijie Su
  In TMLR 2024. [pdf]

2023

• A Law of Data Separation in Deep Learning
  Hangfeng He and Weijie Su
  In PNAS 2023 (direct submission). [pdf] [code]

• On Regularization and Inference with Label Constraints
  Kaifu Wang, Hangfeng He, Tin Nguyen, Piyush Kumar, and Dan Roth
  In ICML 2023. [pdf]

• Incidental Supervision for Natural Language Understanding
  Hangfeng He
  PhD dissertation, University of Pennsylvania, 2023. [pdf]

2022

• Transfer Learning via Representation Learning
  Mohammad Rostami, Hangfeng He, Muhao Chen, and Dan Roth
  In Federated and Transfer Learning 2022 (book chapter). [pdf]

• Weighted Training for Cross-Task Learning
  Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, and Weijie Su (alphabetical order)
  In ICLR 2022 (oral presentation). [pdf] [code]

2021

• Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
  Cong Fang, Hangfeng He, Qi Long, and Weijie Su (alphabetical order)
  In PNAS 2021 (direct submission). [pdf] [code]

• Foreseeing the Benefits of Incidental Supervision
  Hangfeng He, Mingyuan Zhang, Qiang Ning, and Dan Roth
  In EMNLP 2021. [pdf] [code]

• Toward Better Generalization Bounds with Locally Elastic Stability
  Zhun Deng, Hangfeng He, and Weijie Su
  In ICML 2021. [pdf]

2020

• QANom: Question-Answer driven SRL for Nominalizations
  Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Brook Weiss, Hangfeng He, Dan Roth, Luke Zettlemoyer, and Ido Dagan
  In COLING 2020. [pdf]

• Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
  Shuxiao Chen, Hangfeng He, and Weijie Su (alphabetical order)
  In NeurIPS 2020. [pdf] [code]

• Towards Understanding the Dynamics of the First-Order Adversaries
  Zhun Deng, Hangfeng He, Jiaoyang Huang, and Weijie Su
  In ICML 2020. [pdf]

• QuASE: Question-Answer Driven Sentence Encoding
  Hangfeng He, Qiang Ning, and Dan Roth
  In ACL 2020. [pdf] [code] [demo]

• Understanding Spatial Relations through Multiple Modalities
  Soham Dan, Hangfeng He, and Dan Roth
  In LREC 2020 (short papers). [pdf]

• The Local Elasticity of Neural Networks
  Hangfeng He and Weijie Su
  In ICLR 2020. [pdf] [code]

2019

• Partial or Complete, That’s The Question
  Qiang Ning, Hangfeng He, Chuchu Fan, and Dan Roth
  In NAACL-HLT 2019. [pdf]

2018

• Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media: A Unified Model
  Jingjing Xu, Hangfeng He, Xu Sun, Xuancheng Ren, and Sujian Li
  In TASLP 2018. [pdf]

2017

• Detecting negation scope is easy, except when it isn’t
  Federico Fancellu, Adam Lopez, Bonnie Webber, and Hangfeng He
  In EACL 2017 (short papers). [pdf]

• F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media
  Hangfeng He and Xu Sun
  In EACL 2017 (short papers). [pdf]

• A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media
  Hangfeng He and Xu Sun
  In AAAI 2017. [pdf]

• Neural Networks for Negation Cue Detection in Chinese
  Hangfeng He, Federico Fancellu, and Bonnie Webber
  In SemBEaR workshop 2017. [pdf]