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Yu Wan

Yu Wan has 3 papers in our library, in 2022–2026, most of them on Language models & pretraining, Machine translation, Evaluation & benchmarks; often with Baosong Yang, Boyi Deng, Boxing Chen.

3 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 12 · 58 works · 456 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 220 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #6545

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 14 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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 workMachine translation · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.chrF · 1 work

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. UniTE: Unified Translation Evaluation (2022 · 100 citations)
    Machine translation evaluation usually comes in three variants: comparing a hypothesis with a reference (REF), with the source sentence only, also called quality estimation (SRC), or with both (SRC+REF).
  2. Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders (2025 · 18 citations)
    The paper asks how large language models represent individual languages internally. Earlier work relied on single neurons, which suffer from superposition, or on logit-lens readouts, which are unreliable outside the final layers. The authors instead use pretrained Sparse Autoencoders (SAEs), which split…
  3. Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models (2026 · 3 citations)
    Sparse autoencoders (SAEs) break LLM hidden states into a small number of interpretable directions. In practice, though, they have mostly been used to inspect and label features after the fact, not to build or fix models. The Qwen Team addresses this with Qwen-Scope, an openly released collection of SAEs trained on…

All works

7 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. Uncertainty-Aware Curriculum Learning for Neural Machine Translation ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetYikai Zhou et al.2020 · 123 citations
  2. PolyLM: An Open Source Polyglot Large Language Model ↗arXiv:2307.06018 · no summary yetXiangpeng Wei et al.2023 · 103 citations
  3. Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task ↗Proceedings of the Seventh Conference on Machine Translation (WMT) · no summary yetKeqin Bao et al.2022 · 4 citations
  4. Qwen-Scope: Turning Sparse Features into Development Tools for Large Language ModelsarXiv:2605.11887Boyi Deng, Xu Wang, Yaoning Wang, et al.2026 · 3 citations
  5. Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors ↗arXiv:2302.08975 · no summary yetKeqin Bao et al.2023 · 2 citations

 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 220 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

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