Paiheng Xu

徐 徘 衡

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8125 Paint Branch Dr, Room 4104

College Park, Maryland 20742

I am a fourth-year CS Ph.D. student at the University of Maryland’s Computational Linguistics and Information Processing (CLIP) lab, advised by Wei Ai. My research interests are Natural Language Processing (NLP) and Computational Social Science, focusing on education and social media contexts. I am interested in understanding human conversations and developing applications using machine learning and NLP models within these domains, with considerations of causality and fairness. At UMD, I also closely work with Jing Liu, Louiqa Raschid, Vanessa Frias-Martinez, and Furong Huang

In the past, I have interned at Adobe Research. Previously, I graduated from Johns Hopkins University with M.S.E in Computer Science, where I worked with Mark Dredze at Center for Language and Speech Processing. I obtained my B.E in Computer Science from Southwest University, where I worked with Yong Deng on Complex Network, and Tao Zhou on Human Mobility.

News

May 2024 Our paper on concept level spurious correlations for text classification, with Yuhang Zhou, is accepted to ACL 2024.
Mar 2024 Our paper The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education is accepted to NAACL 2024. See you in Mexico City!
Mar 2024 Our survey on Large Language Models and Causal Inference is online now. Feedback is welcomed.
Jan 2024 Our paper Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic is available on Scientific Reports!

Selected Publications

  1. The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education
    Paiheng Xu, Jing Liu , Nathan Jones , Julie Cohen , and Wei Ai
    In NAACL , Jun 2024
  2. Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
    Yuhang Zhou , Paiheng Xu, Xiaoyu Liu , Bang An , Wei Ai , and Furong Huang
    In ACL , Aug 2024
  3. Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic
    Paiheng Xu, David A Broniatowski , and Mark Dredze
    Scientific reports, Jan 2024