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Beizhe Hu

Beizhe Hu has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Danding Wang, Juan Cao, Qiang Sheng.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 5 · 14 works · 326 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 53 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11909

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 · China2023–2026 · 14 paperscurrent🔬Institute of Computing TechnologyBeijing · China2021–2026 · 13 papersEmployment since 2021current🎓University of Chinese Academy of SciencesBeijing · China2023–2026 · 12 paperscurrent

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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 2 worksSociology and Political Science · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments (2026)
    The paper tackles factual hallucination detection in LLMs, framed as binary classification of whether a response is factual.
  2. Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework (2026)
    Automated fact-checking can explain why a claim is false. Extractive explanations, however, tend to be disjointed, and even fluent LLM-written ones ignore who the reader is. Yet whether a correction works depends on whether its audience accepts it. The authors present MURSE, a framework that generates Chinese…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetBeizhe Hu et al.2024 · 277 citations
  2. Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-JudgmentsAnnual Meeting of the Association for Computational Linguistics (ACL)Hao Mi, Qiang Sheng, Shaofei Wang, et al.2026
  3. Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced FrameworkConference of the European Chapter of the Association for Computational Linguistics (EACL)Xinle Pang, Danding Wang, Qiang Sheng, et al.2026

 Co-authors here

5 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.

 Cited by

Papers of our library cite their works 53 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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