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Yingshui Tan

Yingshui Tan has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Bias, fairness & safety, Evaluation & benchmarks; often with Jiaheng Liu, Bo Zheng, Boren Zheng.

4 papers in our library · 2024–2026

OpenAlex · h-index 6 · 37 works · 101 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 49 times in our library · ⭐ Paper Stars ranking: #7699

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

🏢Alibaba GroupHangzhou · China2024–2026 · 14 paperscurrent🏢Alibaba GroupSan Mateo · United States2026 · 2 paperscurrent🎓University of California, BerkeleyBerkeley · United States2019–2021 · 5 papers🎓RWTH Aachen UniversityAachen · Germany2019 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 4 worksBias, fairness & safety · 3 worksVision & language · 2 worksEvaluation & benchmarks · 2 works

 Works by year

2020s

20261 paper20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2024 · 34 citations)
    Hallucination remains a major weakness of large language models. Factuality is hard to measure when models produce long answers that mix many claims. OpenAI's SimpleQA addressed this with short fact-seeking questions, but it covers only English. Chinese SimpleQA is a Chinese counterpart: 3,000 short questions, each…
  2. HiddenDetect: Detecting Jailbreak Attacks against Multimodal Large Language Models via Monitoring Hidden States (2025 · 15 citations)
    Vision-language models (LVLMs) are easier to jailbreak than text-only LLMs. Common defenses rely on safety fine-tuning or protective prompts, which are costly or tend to over-refuse. This paper asks whether a model's own hidden states already show that a prompt is harmful before any output is generated.
  3. Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models (2024 · 4 citations)
    Models deployed in China must handle questions about local law, policy and ethics correctly. Existing safety benchmarks mostly use red-teaming or case-based prompts that mix several risks at once, which makes it hard to tell which areas of safety knowledge a model lacks. The authors introduce Chinese SafetyQA, a…
  4. USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026 · 1 citation)
    Existing safety benchmarks for multimodal large language models (MLLMs) cover few risk types and contain few samples.

 Co-authors here

12 of 17 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 49 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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