Publications

2026

• Small Arguments, Big Ideas: Fostering Children’s Collaborative Reasoning with LLM Agents
  Hecong Wang, Meng Wang, Erfan Farhadi, Asad Shahab, Yuanzhu Li, Haozheng Du, Carolyn Rose, Hangfeng He, and Zhen Bai
  In ISLS 2026 (to appear).

• Ranking Human and LLM Texts Using Locality Statistics
  Yiyang Wang, Chen Ding, and Hangfeng He
  In EACL 2026 (to appear).

2025

• How LLMs Fail to Support Fact-Checking
  Adiba Proma, Neeley Pate, James Druckman, Gourab Ghoshal, Hangfeng He, and Ehsan Hoque
  In IDeaS 2025. [pdf]

• Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Prediction
  Huanxin Sheng, Xinyi Liu, Hangfeng He, Jieyu Zhao, and Jian Kang
  In EMNLP 2025 (SAC Highlights). [pdf]

• The Role of Model Confidence on Bias Effects in Measured Uncertainties
  Xinyi Liu, Weiguang Wang, and Hangfeng He
  In EMNLP 2025 (findings). [pdf]

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

• On the Role of Model Prior in Real-World Inductive Reasoning
  Zhuo Liu, Ding Yu, and Hangfeng He
  In EMNLP 2025. [pdf]

• A Law of Next-Token Prediction in Large Language Models
  Hangfeng He and Weijie Su
  In PRE 2025. [pdf] [code]

• 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]

Preprints

• Replicating Human Motivated Reasoning Studies with LLMs
  Neeley Pate, Adiba Mahbub Proma, Hangfeng He, James N Druckman, Daniel Molden, Gourab Ghoshal, and Ehsan Hoque
  In arXiv 2026. [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]

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