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Wei Hu

Wei Hu has 3 papers in our library, in 2025–2026, most of them on Question answering, Reasoning, Information retrieval & search; often with Chen Shen, Huan Zhang, Lechen Zhang.

3 papers in our library · 2025–2026

OpenAlex · h-index 0 · 6 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 9 times in our library · ⭐ Paper Stars ranking: #10567

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

🎓Nanjing UniversityNanjing · China2026 · 3 paperscurrent🎓University of MichiganAnn Arbor · United States2026 · 2 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 7The 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 · 2 worksSoftware · 1 work

🧭 Topics 4Research subjects and languages the work is about.Information retrieval & search · 1 workQuestion answering · 1 workEfficient models & compression · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025 · 16 citations)
    In retrieval-augmented multi-hop QA, LLMs often split a complex question into sub-questions. Later sub-questions then refer to an earlier result through a pronoun such as "this author" instead of naming the entity. The authors call this failure "lost-in-retrieval." Without the key entity, retrieval for these…
  2. Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026)
    Translating code between several programming languages with LLMs runs into two problems. Parallel code that comes with executable tests is scarce, and training on many language pairs at once tends to favor the easier directions. BootTrans tackles both with reinforcement learning from verifiable rewards. It starts…
  3. Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation (2026)
    Distilling reasoning from large models such as DeepSeek-R1 into smaller LLMs usually relies on large SFT datasets in which every example counts the same.

 Co-authors here

8 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 9 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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