Wenhao Zhu
Wenhao Zhu has 4 papers in our library, in 2024–2026, most of them on Reasoning, Multilingual & cross-lingual NLP, Machine translation; often with Shujian Huang, Jiajun Chen, Lei Li.
OpenAlex · ORCID · h-index 7 · 38 works · 433 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 400 times in our library · ⭐ Paper Stars ranking: #3505
Affiliations
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works
🧭 Topics 4Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 2 worksMachine translation · 1 workReasoning · 3 worksLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024 · 506 citations)
The paper asks how far large language models have come as general-purpose multilingual translators and what drives their translation quality under in-context learning. - MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024 · 59 citations)
Large language models tend to solve math word problems much better in English than in other languages, mainly because their training data is skewed toward English. - NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons (2026 · 13 citations)
Large Reasoning Models still make three kinds of mistakes. Within a step, they get calculations or derivations wrong. Across steps, they loop or stall. At the instance level, they over-think easy questions. Earlier fixes target only one of these, rely on opaque RL training, and offer little insight or control. - 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.
Co-authors here
- Shujian Huang ×3
- Jiajun Chen ×2
- Lei Li ×2
- Changjiang Gao
- Fei Yuan
- Guojie Song
- Haonan Dong
- Haoran Ye
- Hongyi Liu
- Jingjing Xu
- Kehan Jiang
- Lingpeng Kong
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026, ACL 2024
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
Cited by
Updated
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