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Siyuan Wu

Siyuan Wu has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Evaluation & benchmarks, Bias, fairness & safety; often with Lichao Sun, Qihui Zhang, Yuan Li.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 7 · 19 works · 135 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 274 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5405

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

🎓Xiamen UniversityXiamen · China2005–2025 · 7 paperscurrent🎓Nanjing University of Aeronautics and AstronauticsNanjing · China2006–2025 · 5 paperscurrent🎓Harbin UniversityHarbin · China2025 · 1 papercurrent🎓National University of SingaporeSingapore · Singapore2006 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksSafety Research · 1 workSocial Sciences · 1 workArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TrustLLM: Trustworthiness in Large Language Models (2024 · 237 citations)
    TrustLLM tackles the lack of a unified way to judge whether large language models behave reliably and responsibly.
  2. MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use (2023 · 81 citations)
    Most earlier tool-use benchmarks for LLMs test how a model fills in tool arguments and handles tool outputs.

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. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
  2. MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to UsearXiv:2310.03128Yue Huang, Jiawen Shi, Yuan Li, et al.2023 · 81 citations
  3. On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective ↗arXiv (Cornell University) · no summary yetY. Huang et al.2025 · 16 citations

 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 274 times (of the 149,439 papers whose bibliographies we read); 73 citing papers with a page here →.

Updated

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