--- title: "Yuxiang Wang — author profile and papers — Elamur Research" description: "Yuxiang Wang, Chinese University of Hong Kong, Shenzhen: 4 papers on Elamur Research (2026), h-index 10 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/yuxiang-wang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yuxiang-wang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yuxiang Wang # Yuxiang Wang Yuxiang Wang has 4 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/), [Prayag Tiwari](https://md.research.elamur.ai/free/en/authors/prayag-tiwari/), [Shengzhi LI](https://md.research.elamur.ai/free/en/authors/shengzhi-li/). 4 papers in our library · 2026 [OpenAlex](https://openalex.org/A5100394395) · [ORCID](https://orcid.org/0000-0002-7020-6843) · [**h-index 10**](https://openalex.org/authors/A5100394395) · [34 works](https://openalex.org/works?filter=author.id:A5100394395) · [347 citations](https://openalex.org/works?filter=author.id:A5100394395&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/yuxiang-wang/#papers) · [🔗 Cited 4 times in our library](https://md.research.elamur.ai/free/en/authors/yuxiang-wang/cited-by/) · [⭐ Paper Stars ranking: #10343](https://md.research.elamur.ai/free/en/paper-stars/page/104/#star-10343) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yuxiang+Wang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yuxiang+Wang) · [dblp](https://dblp.org/search?q=Yuxiang+Wang) · [ACL Anthology](https://aclanthology.org/search/?q=Yuxiang+Wang) ## Affiliations [🎓**Chinese University of Hong Kong, Shenzhen** Shenzhen · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong-shenzhen/) · [🎓**Zhejiang University** Hangzhou · China 2019–2020 · 7 papers](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) · [🎓**Hangzhou Dianzi University** Hangzhou · China 2023–2024 · 3 papers](https://md.research.elamur.ai/free/en/universities/hangzhou-dianzi-university/) · [🎓**Jilin University** Changchun · China 2025 · 2 papers](https://md.research.elamur.ai/free/en/universities/jilin-university/) · [🎓**Anhui Polytechnic University** Wuhu · China 2024 · 2 papers](https://ror.org/041sj0284) · [🎓**Zhejiang University of Technology** Hangzhou · China 2021–2022 · 2 papers](https://md.research.elamur.ai/free/en/universities/zhejiang-university-of-technology/) · [🔬**State Key Laboratory Fluid Power and Mechatronic Systems** Hangzhou · China 2019–2020 · 2 papers](https://ror.org/01h0g5x34) · [🎓**Nanjing University of Information Science and Technology** Nanjing · China 2018–2019 · 2 papers](https://ror.org/02y0rxk19) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 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 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 4 Research subjects and languages the work is about.[Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/) ## Works by year ### 2020s [**2026** 4 papers](https://md.research.elamur.ai/free/en/authors/yuxiang-wang/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables](https://md.research.elamur.ai/free/en/papers/ye-2026-when-tableqa-meets-noise-dual/)** *(2026 · 4 citations)* This paper addresses how large language models lose accuracy in table question answering (TableQA) when questions are complex and tables are large. 2. **[DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization](https://md.research.elamur.ai/free/en/papers/shen-2026-dragon-domain-specific-robust-automatic/)** *(2026 · 1 citation)* Because their training data mostly comes from broad, general sources like Wikipedia, dense retrievers have trouble with specialized corpora. 3. **[Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation](https://md.research.elamur.ai/free/en/papers/wang-2026-exploring-graph-learning-tasks-pure/)** *(2026 · 1 citation)* LLM-based graph learning studies use inconsistent datasets, preprocessing and splits, so it remains unclear how pure LLMs compare with dedicated graph models. 4. **[Preference Optimization for Review Question Generation Improves Writing Quality](https://md.research.elamur.ai/free/en/papers/sharma-2026-preference-optimization-review-question-generation/)** *(2026)* LLM-drafted peer-review questions are often shallow. They tend to echo keywords or the introduction instead of engaging critically with the whole paper. To address this, the authors built a corpus of reviewer questions from ICLR 2024 reviews on OpenReview, using Gemini 2.0 for extraction and olmOCR for paper text.… ## Co-authors here 5 co-authors with a page, the most papers together first. - [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/) - [Prayag Tiwari](https://md.research.elamur.ai/free/en/authors/prayag-tiwari/) - [Shengzhi LI](https://md.research.elamur.ai/free/en/authors/shengzhi-li/) - [Wenqi Fan](https://md.research.elamur.ai/free/en/authors/wenqi-fan/) - [Yao Ma](https://md.research.elamur.ai/free/en/authors/yao-ma/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×3: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×3 - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ## Cited by Papers of our library cite their works 4 times (of the 149,439 papers whose bibliographies we read); [2 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yuxiang-wang/cited-by/). Updated 1 November 2026