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Yuhao Wang

Yuhao Wang has 4 papers in our library, in 2024–2026, most of them on Information retrieval & search, Language models & pretraining, Question answering; often with Jing Liu, Ruiyang Ren, Haifeng Wang.

4 papers in our library · 2024–2026

OpenAlex · h-index 2 · 5 works · 47 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 115 times in our library · ⭐ Paper Stars ranking: #6091

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

🎓Jiangsu University of Science and TechnologyZhenjiang · Chinacurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksComputer Vision and Pattern Recognition · 4 worksLanguage and Linguistics · 4 worksInformation Systems · 3 works

🧭 Topics 4Research subjects and languages the work is about.Information retrieval & search · 3 worksLanguage models & pretraining · 1 workQuestion answering · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation (2025 · 101 citations)
    This COLING 2025 study asks whether large language models know where their factual knowledge ends, and how retrieval augmentation changes that self-awareness in open-domain QA.
  2. REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering (2024 · 55 citations)
    Retrieval-augmented generation (RAG) helps large language models answer open-domain questions. However, the retrieved passages often include irrelevant material that can lead the model to wrong answers. Earlier robustness methods such as Self-RAG and RobustLM judge relevance only with binary labels, which gives…
  3. Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation (2026 · 2 citations)
    Reinforcement learning has improved LLMs on math and short-answer QA, where outputs can be checked against a single correct target.
  4. When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs (2026)
    Most MLLM benchmarks give the model every clue up front and ask for a single answer. This paper asks whether models can instead seek out missing evidence on their own. The authors frame this as a guessing game. A model sees a pool of candidate images, gets no hints about which one is the target, and must ask yes/no…

 Co-authors here

12 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 115 times (of the 149,439 papers whose bibliographies we read); 32 citing papers with a page here →.

Updated

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