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James Y. Huang

James Y. Huang has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Bias, fairness & safety, Vision & language; often with Fei Wang, Hoifung Poon, Muhao Chen.

3 papers in our library · 2024–2026

OpenAlex · h-index 5 · 18 works · 82 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 130 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #6923

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

🎓University of California, DavisDavis · United States2026 · 1 papercurrent🎓University of Southern CaliforniaLos Angeles · United States2022–2025 · 8 papers🎓University of California, Los AngelesLos Angeles · United States2021–2024 · 3 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 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

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024 · 50 citations)
    Applying DPO to multimodal LLMs often yields inconsistent gains, and earlier work mostly blamed the quality of preference data.
  2. DeAL: Decoding-time Alignment for Large Language Models (2025 · 36 citations)
    The paper argues that alignment learned during training (e.g., RLHF) has two weaknesses. It locks models into a developer's fixed, supposedly universal preferences, and it can be bypassed, for example through jailbreaks. The authors propose DeAL, which moves alignment to inference time. Decoding is treated as an…
  3. MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026 · 7 citations)
    LLMs often tackle hard problems by replaying reasoning patterns absorbed during training rather than deliberately choosing a suitable way to think.

All works

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

  1. mDPO: Conditional Preference Optimization for Multimodal Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Fei Wang, Wenxuan Zhou, James Y. Huang, et al.2024 · 50 citations
  2. MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding ↗arXiv (Cornell University) · no summary yetFei Wang et al.2024 · 48 citations
  3. DeAL: Decoding-time Alignment for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)James Y. Huang, Sailik Sengupta, Daniele Bonadiman, et al.2025 · 36 citations
  4. Offset Unlearning for Large Language Models ↗arXiv (Cornell University) · no summary yetJames Y. Huang et al.2024 · 17 citations
  5. MetaScale: Test-Time Scaling with Evolving Meta-ThoughtsFindings of the Association for Computational LinguisticsQin Liu, Wenxuan Zhou, Nan Xu, et al.2026 · 7 citations
  6. Video Models Can Reason with Verifiable Rewards ↗arXiv (Cornell University) · no summary yetTinghui Zhu et al.2026

 Co-authors here

12 of 13 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 130 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

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