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Jiuhai Chen

Jiuhai Chen has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Jianzong Wang, Jing Xiao, Lichang Chen.

2 papers in our library · 2023–2024

OpenAlex · h-index 8 · 26 works · 198 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 262 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5415

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 Maryland, College ParkCollege Park · United States2021–2025 · 21 paperscurrent🏢SalesforceSan Francisco · United States2025 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2023 · 161 citations)
    LIMA showed that instruction tuning depends more on data quality than on volume. Picking good samples out of large open instruction sets still usually takes human curation or an external judge such as ChatGPT. This paper lets the target model choose its own training data. First, a base LLM gets a short warm-up…
  2. Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness (2024 · 72 citations)
    Commercial LLMs reached through black-box APIs often give wrong answers with apparent confidence. Users usually cannot see token probabilities or training data, and they have no calibration set. Chen and Mueller propose BSDetector, a training-free wrapper that adds a numeric confidence estimate to any response the…

All works

4 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. From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction TuningarXiv:2308.12032Ming Li, Yong Zhang, Zhitao Li, et al.2023 · 161 citations
  2. Quantifying Uncertainty in Answers from any Language Model and Enhancing their TrustworthinessAnnual Meeting of the Association for Computational Linguistics (ACL)Jiuhai Chen, Jonas Mueller2024 · 72 citations
  3. ODIN: Disentangled Reward Mitigates Hacking in RLHF ↗arXiv:2402.07319 · no summary yetLichang Chen et al.2024 · 38 citations
  4. BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset ↗arXiv (Cornell University) · no summary yetJiuhai Chen et al.2025 · 33 citations

 Co-authors here

8 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 262 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

Updated

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