I am a rising 3rd year CS Ph.D. candidate at the LAUNCH Lab, University of Michigan – Ann Arbor, advised by Prof. Lu Wang. I obtained my Bachelor’s degree from Peking University, advised by Prof. Xiaojun Wan.

My research aims to reduce hallucinations and improve the reasoning capabilities of language models (LMs).

News

• 2024-05: One paper is accepted to Findings of ACL 2024 on self-correction of small LMs! See you in Bangkok, Thailand.
• 2024-05: Pleased to start my 1st PhD internship at Microsoft Research, Montreal. I will work with Eric Yuan and Alessandro Sordoni.
• 2023-10: One paper is accepted to EMNLP 2023 on knowledge conflicts for LLMs!

Publications

(* denotes equal contribution)

• Small Language Models Need Strong Verifiers to Self-Correct Reasoning
  Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang
  Findings of ACL 2024 [paper] [code] [project page]
• Merging Generated and Retrieved Knowledge for Open-Domain QA
  Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang
  EMNLP 2023 [paper] [code]
• SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
  Yunxiang Zhang, Xiaojun Wan
  NeurIPS 2023 Datasets and Benchmarks Track [paper] [data]
• Investigating Zero- and Few-shot Generalization in Fact Verification
  Liangming Pan*, Yunxiang Zhang*, Min-Yen Kan
  AACL-IJCNLP 2023 [paper]
• MOVER: Mask, Over-generate and Rank for Hyperbole Generation
  Yunxiang Zhang, Xiaojun Wan
  NAACL 2022 [paper] [code]
• Interpreting the Robustness of Neural NLP Models to Textual Perturbations
  Yunxiang Zhang, Liangming Pan, Samson Tan, Min-Yen Kan
  Findings of ACL 2022 [paper]
• BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles
  Yunxiang Zhang, Xiaojun Wan
  AAAI 2022 [paper] [data]

Services

Program Committee Member / Reviewer: AISTATS (2025), ICLR (2025), ACL Rolling Review (June 2024), NeurIPS (2024), ACL 2024 KnowledgeNLP Workshop, COLM (2024), EMNLP (2022, 2023), ACL (2023), CoNLL (2023, 2024), ML4Health (2023), ACM Computing Surveys