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Qinhong Zhou

Qinhong Zhou has 2 papers in our library, in 2023, most of them on Language models & pretraining; often with Chuang Gan, David Cox, Hongxin Zhang.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 4 · 13 works · 174 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 259 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5125

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 Massachusetts AmherstAmherst Center · United States2023–2028 · 5 papersEducation 2024–2028 · PhD Student, Computer Sciencecurrent🎓Tsinghua UniversityBeijing · China2016–2023 · 3 papersEducation 2016–2020 · Bachelor, Computer Science and TechnologyEducation 2020–2023 · Master, Computer Science and Technology

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 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 · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision (2023 · 236 citations)
    Popular AI assistants are usually aligned with supervised fine-tuning and RLHF. Both need large amounts of human annotation, which is expensive and can be inconsistent or biased. Open models such as Alpaca and Vicuna avoid that cost by distilling outputs from already-aligned commercial systems, so they still rely on…
  2. SALMON: Self-Alignment with Instructable Reward Models (2023 · 41 citations)
    The paper tackles the heavy cost of aligning large language models with SFT plus RLHF, which needs many human-written demonstrations and preference labels.

All works

3 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. Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human SupervisionConference on Neural Information Processing Systems (NeurIPS)Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.2023 · 236 citations
  2. Building Cooperative Embodied Agents Modularly with Large Language Models ↗arXiv:2307.02485 · no summary yetHongxin Zhang et al.2023 · 95 citations
  3. SALMON: Self-Alignment with Instructable Reward ModelsarXiv:2310.05910Zhiqing Sun, Yikang Shen, Hongxin Zhang, et al.2023 · 41 citations

 Co-authors here

7 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 259 times (of the 149,439 papers whose bibliographies we read); 67 citing papers with a page here →.

Updated

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