🧑‍🔬 Haixu Tang

🏛️ Affiliations

🎓Indiana University BloomingtonBloomington · United States2004–2026 · 226 papersEmployment 2004–2010 · Assistant Professor, School of Informatics and ComputingEmployment 2010–2015 · Associate Professor, School of Informatics and ComputingEmployment since 2015 · Professor, School of Informatics and Computingcurrent🎓Indiana UniversityBloomington · United States2005–2026 · 198 paperscurrent🎓University of California San DiegoSan Diego · United States2001–2004 · 7 papersEmployment 2001–2004 · Assistant Project Scientist, Department of Computer Science and Engineering🏛️Chinese Academy of SciencesBeijing · China1993–2025 · 6 papersEducation 1993–1998 · Ph.D, Shanghai Institute of Biochemistry🎓University of Southern CaliforniaLos Angeles · United States1999–2001 · 4 papersEmployment 1999–2001 · Research Associate, Department of Mathematics🔬Shanghai Institutes for Biological SciencesShanghai · China2002–2009 · 3 papers🎓Nanjing UniversityNanjing · China1989–1993Education 1989–1993 · B. S., Department of Physics

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Hey, That's My Data! Token-Only Dataset Inference in Large Language Models (2026 · 1 citation)
   Owners who suspect an LLM was trained on their proprietary or copyrighted text usually need log-probabilities to test this. Many commercial APIs, however, return only greedy top-1 tokens. The authors address this token-only setting with CatShift, which uses catastrophic forgetting as an auditing signal.
2. Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026)
   The paper addresses a dilemma in using LLMs for sensitive work. Cloud-hosted models such as GPT-5 give strong answers, but sending them raw queries can reveal confidential research or legal intent. Purely local models keep data private but answer less well. The authors propose GTKA, a client-side, semantic-level…

🤝 Co-authors here

11 co-authors with a page, the most papers together first.

• Ang Li
• Jingwei Xiong
• Pin-Yu Chen
• Rujing Yao
• Tsung-Yi Ho
• XiaoFeng Wang
• Xiaozhong Liu
• Yang Wu
• Yufei Shi
• Zhuoren Jiang
• Zihao Wang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2

Updated 2 November 2026

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