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Luxi He

Luxi He has 2 papers in our library, in 2024, most of them on Language models & pretraining, Bias, fairness & safety, Vision & language; often with Alexis Chevalier, Bo Li, Boyi Wei.

2 papers in our library · 2024

OpenAlex · h-index 2 · 7 works · 11 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 185 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #6405

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

🎓Princeton UniversityPrinceton · United States2024–2026 · 6 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workSafety Research · 1 workSocial Sciences · 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 worksVision & language · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs (2024 · 119 citations)
    Benchmarks commonly used to evaluate chart understanding in multimodal LLMs, such as FigureQA, DVQA and ChartQA, rely on synthetic or visually uniform charts and on template-generated questions.
  2. SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal (2024 · 87 citations)
    SORRY-Bench is a benchmark that measures how reliably aligned LLMs decline unsafe requests. The authors identify three weaknesses in earlier safety evaluations. Their category schemes are coarse and inconsistent, and they concentrate on a few topics such as fraud while barely testing others like self-harm. They…

All works

6 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. CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMsConference on Neural Information Processing Systems (NeurIPS)Zirui Wang, Mengzhou Xia, Luxi He, et al.2024 · 119 citations
  2. SORRY-Bench: Systematically Evaluating Large Language Model Safety RefusalarXiv:2406.14598Tinghao Xie, Xiangyu Qi, Yi Zeng, et al.2024 · 87 citations
  3. Fantastic Copyrighted Beasts and How (Not) to Generate Them ↗arXiv (Cornell University) · no summary yetLuxi He et al.2024 · 9 citations
  4. AI Risk Management Should Incorporate Both Safety and Security ↗arXiv (Cornell University) · no summary yetXiangyu Qi et al.2024 · 3 citations
  5. Statutory construction and interpretation for AI ↗Proceedings of the National Academy of Sciences · no summary yetLuxi He et al.2026 · 1 citation
  6. The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Privilege ↗Proceedings of the AAAI/ACM Conference on AI Ethics and Society · no summary yetLuxi He et al.2025 · 1 citation

 Co-authors here

12 of 18 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 185 times (of the 149,439 papers whose bibliographies we read); 62 citing papers with a page here →.

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