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Yuchun Fan

Yuchun Fan has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Reasoning; often with Jingbo Zhu, Lei Huang, Tong Xiao.

3 papers in our library · 2025–2026

OpenAlex · h-index 3 · 6 works · 34 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 29 times in our library · ⭐ Paper Stars ranking: #8345

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

🎓Northeastern UniversityShenyang · China2023–2026 · 3 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 6The 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 · 2 worksInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & cross-lingual NLP · 1 workReasoning · 1 workInformation retrieval & search · 1 work

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025 · 20 citations)
    Large language models reason well in English but much less reliably in other languages, especially low-resource ones.
  2. Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025 · 11 citations)
    The paper addresses faithfulness hallucination in retrieval-augmented long-form question answering (LFQA), where a model writes paragraph-length answers containing claims that the retrieved documents do not support or that contradict them.
  3. Cross-layer Attention Sharing for Pre-trained Large Language Models (2026 · 2 citations)
    Earlier work on making LLM attention more efficient mostly targets redundancy inside a layer, for example by compressing the KV cache or grouping heads.

 Co-authors here

12 of 24 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 29 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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