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Xuehai Tang

Xuehai Tang has 4 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Biyu Zhou, Jizhong Han, Songlin Hu.

4 papers in our library · 2026

OpenAlex · ORCID · h-index 9 · 63 works · 227 citations OpenAlex, 2026-10-14

📄 4 works in our library · ⭐ Paper Stars ranking: #11805

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏛️Chinese Academy of SciencesBeijing · China2014–2026 · 38 paperscurrent🔬Institute of Information EngineeringBeijing · China2014–2026 · 38 paperscurrent🎓Anhui Agricultural UniversityHefei · China2020–2026 · 10 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2021–2025 · 10 papers🏛️State Forestry and Grassland AdministrationBeijing · China2020–2024 · 3 papers🎓Beijing Forestry UniversityBeijing · China2010–2011 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 5Research subjects and languages the work is about.Bias, fairness & safety · 2 worksLanguage models & pretraining · 2 worksReasoning · 2 worksEvaluation & benchmarks · 1 workSpeech & spoken language · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing (2026)
    Current unstructured model editing methods, such as UnKE and AnyEdit, can make an LLM reproduce an edited passage in full.
  2. HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026)
    Audio Large Language Models (ALLMs) are trained on huge amounts of speech. The authors ask whether this training lets them work out private facts about a speaker from the voice alone, with no help from what is actually said. To test this, they built HearSay, a benchmark of more than 22,000 real recordings. Most clips…
  3. More Thinking, Less Talking: Internalizing Deliberative Safety into LLM Parameters (2026)
    Explicit Safety-oriented Chain-of-Thought (SCoT) helps LLMs resist jailbreaks. However, the visible reasoning trace creates its own risks: it can leak harmful details before the model reaches a refusal, and it shows attackers how the model's safety logic works. The authors ask whether the protection of deliberative…
  4. Resolving the Security-Auditability Dilemma with Auditable Latent Chain-of-Thought Alignment (2026)
    Reasoning-based safety alignment has LLMs write out their safety deliberation as a Chain-of-Thought (CoT), which makes supervision and auditing possible.

 Co-authors here

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

Venues

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

Updated

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