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Fei Yuan

Fei Yuan has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Lei Li, Changjiang Gao, Qipeng Guo.

2 papers in our library · 2025–2026

OpenAlex · h-index 3 · 11 works · 18 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 74 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9791

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

🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2023–2026 · 9 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Could Thinking Multilingually Empower LLM Reasoning? (2025 · 10 citations)
    LLMs are usually assumed to reason best in English. Recent evidence shows that some non-English languages can outperform English on reasoning benchmarks. This paper asks how much accuracy could be gained by having a model reason over the same question in many languages and pooling the results.
  2. CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026 · 1 citation)
    Training code-generation LLMs requires large numbers of instruction–code pairs. Manual curation is expensive, and popular automatic methods such as Self-Instruct and Evol-Instruct depend on fixed prompting heuristics. As a result, they often produce vague tasks and code that fails to compile or pass its tests.…

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. Extrapolating Large Language Models to Non-English by Aligning Languages ↗arXiv:2308.04948 · no summary yetWenhao Zhu et al.2023 · 71 citations
  2. Could Thinking Multilingually Empower LLM Reasoning?arXiv:2504.11833Changjiang Gao, Xu Huang, Wenhao Zhu, et al.2025 · 10 citations
  3. CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative FeedbackAnnual Meeting of the Association for Computational Linguistics (ACL)Qiushi Sun, Jingyang Gong, Lei Li, et al.2026 · 1 citation

 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 74 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

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