🧑‍🔬 Xiaobao Wu

🏛️ Affiliations

🎓Nanyang Technological UniversitySingapore · Singapore2022–2026 · 46 papersEmployment 2025–2026current🎓Shanghai Jiao Tong UniversityShanghai · China2025–2026 · 4 papersEmployment since 2026 · School of Computer Sciencecurrent🎓University of California, Santa BarbaraSanta Barbara · United States2024–2025 · 4 papers🎓Tsinghua UniversityBeijing · China2019–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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. Fact-Checking Complex Claims with Program-Guided Reasoning (2023 · 146 citations)
   Verifying real-world claims often means combining several pieces of evidence through multi-step reasoning.
2. RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios (2025 · 21 citations)
   Benchmarks of LLM instruction-following mostly check style constraints such as format or length, or they test simple first-order logic. RuleArena instead tests whether models can reason under real regulations written in natural language. The authors gathered 95 rules from three domains:
3. AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge (2025 · 12 citations)
   Large language models are usually tested on public, static benchmarks, and those test items can leak into the training data of later models, which inflates their scores.
4. MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026 · 8 citations)
   Test-time scaling (TTS) makes LLMs reason better but tends to cause overthinking: extra compute goes to every reasoning step, including trivial ones.
5. P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs (2026 · 3 citations)
   Fine-tuning LLMs on third-party or outsourced data exposes them to data-poisoning backdoors. Most existing defenses filter or sanitize inputs and only work against particular trigger styles or task types. Onion targets character-level triggers, while PDB and PSIM are limited to classification. The authors propose…
6. Learning Uncertainty from Sequential Internal Dispersion in Large Language Models (2026)
   The paper tackles hallucination detection in LLMs through uncertainty estimation. Existing internal-state methods have two weaknesses. Some, such as Chain-of-Embeddings (CoE), assume a fixed way hidden states should evolve between layers. Others reduce a response to its last or average token and lose sequence-level…

All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

1. Fact-Checking Complex Claims with Program-Guided ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Liangming Pan, Xiaobao Wu, Xinyuan Lu, et al.2023 · 146 citations
2. Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetXiaobao Wu et al.2020 · 84 citations
3. RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World ScenariosAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiwen Zhou, Wenyue Hua, Liangming Pan, et al.2025 · 21 citations
4. AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World KnowledgeAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaobao Wu, Liangming Pan, Yuxi Xie, et al.2025 · 12 citations
5. MUR: Momentum Uncertainty guided Reasoning for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hang Yan, Fangzhi Xu, Rongman Xu, et al.2026 · 8 citations
6. P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMsFindings of the Association for Computational LinguisticsShuai Zhao, Xinyi Wu, Shiqian Zhao, et al.2026 · 3 citations
7. Learning Uncertainty from Sequential Internal Dispersion in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ponhvoan Srey, Xiaobao Wu, Cong-Duy T Nguyen, et al.2026

🤝 Co-authors here

12 of 21 co-authors with a page, the most papers together first.

• Anh Tuan Luu ×5
• Liangming Pan ×3
• William Yang Wang ×3
• Ruiwen Zhou ×2
• Shuai Zhao ×2
• Fangzhi Xu
• Haiteng Zhao
• Hang Yan
• Haoran Luo
• Jian Zhang
• Jun Liu
• Min-Yen Kan

Venues

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

• Annual Meeting of the Association for Computational Linguistics (ACL) ×5: ACL 2026 ×2, ACL 2025 ×2, ACL 2023
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

🔗 Cited by

Papers of our library cite their works 151 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated 1 November 2026

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