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.
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
Affiliations
Fields, topics and concepts
📚 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
- 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). - 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… - 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
- 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
- PolyLM: An Open Source Polyglot Large Language Model ↗arXiv:2307.06018 · no summary yetXiangpeng Wei et al.2023 · 103 citations
- UniTE: Unified Translation EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Yu Wan, Dayiheng Liu, Baosong Yang, et al.2022 · 100 citations
- Unveiling Language-Specific Features in Large Language Models via Sparse AutoencodersAnnual Meeting of the Association for Computational Linguistics (ACL)Boyi Deng, Yu Wan, Baosong Yang, et al.2025 · 18 citations
- 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
- Qwen-Scope: Turning Sparse Features into Development Tools for Large Language ModelsarXiv:2605.11887Boyi Deng, Xu Wang, Yaoning Wang, et al.2026 · 3 citations
- 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
- Baosong Yang ×3
- Boyi Deng ×2
- Boxing Chen
- Dayiheng Liu
- Derek F. Wong
- Fuli Feng
- Haibo Zhang
- Haoran Wei
- Huan Lin
- Jialong Tang
- Lidia S. Chao
- Xu Wang
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2025, ACL 2022
- Proceedings of the Seventh Conference on Machine Translation (WMT)
Cited by
Updated
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