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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.

4 papers in our library · 2024–2026

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

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. 8 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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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.
  3. 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.
  4. 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

12 of 17 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 400 times (of the 149,439 papers whose bibliographies we read); 88 citing papers with a page here →.

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