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Boren Zheng

Boren Zheng has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Bias, fairness & safety, Evaluation & benchmarks; often with Jiaheng Liu, Yancheng He, Yingshui Tan.

3 papers in our library · 2024–2025

OpenAlex · h-index 1 · 13 works · 8 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 102 times in our library · ⭐ Paper Stars ranking: #6542

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 · 6 paperscurrent🎓Tsinghua UniversityBeijing · China2026 · 1 papercurrent🎓University of Rhode IslandKingston · United States2019–2021 · 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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksSafety Research · 1 workSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025 · 83 citations)
    Safety alignment of large language models depends on preference data and fine-grained harm labels, which are expensive to collect at scale.
  2. 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…
  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…

 Co-authors here

12 of 25 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 102 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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