--- title: "Qiong Wu — author profile and papers · Question answering" description: "Qiong Wu, AT&T: 2 papers on Elamur Research (2026), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/qiong-wu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/qiong-wu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Qiong Wu # Qiong Wu Qiong Wu has 2 papers in our library, in 2026, most of them on [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Dongyan Zhao](https://md.research.elamur.ai/free/en/authors/dongyan-zhao/), [Kai He](https://md.research.elamur.ai/free/en/authors/kai-he/), [Qian Ma](https://md.research.elamur.ai/free/en/authors/qian-ma/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5139771106) · [**h-index 1**](https://openalex.org/authors/A5139771106) · [3 works](https://openalex.org/works?filter=author.id:A5139771106) · [1 citations](https://openalex.org/works?filter=author.id:A5139771106&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/qiong-wu/#papers) · [⭐ Paper Stars ranking: #13095](https://md.research.elamur.ai/free/en/paper-stars/page/131/#star-13095) 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=Qiong+Wu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Qiong+Wu) · [dblp](https://dblp.org/search?q=Qiong+Wu) · [ACL Anthology](https://aclanthology.org/search/?q=Qiong+Wu) ## Affiliations [🏢**AT&T** Bedminster · United States 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/companies/at-t/) · [🎓**Xiamen University** Xiamen · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/xiamen-university/) 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 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 3 Research subjects and languages the work is about.[Vision & language · 1 work](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/qiong-wu/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification](https://md.research.elamur.ai/free/en/papers/ma-2026-ground-then-rank-revisiting-knowledge/)** *(2026 · 1 citation)* Knowledge-based VQA requires facts that the image alone does not show. Multimodal LLMs often fail at it because they must recognise the exact real-world entity and then find the right supporting passage. The authors argue that most MM-RAG pipelines merge these two grounding steps into one costly, trained multimodal… 2. **[Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation](https://md.research.elamur.ai/free/en/papers/wu-2026-curriculum-learning-based-hierarchical-scoring/)** *(2026)* Evaluating question answering (QA) outputs from LLMs at scale is hard. Lexical metrics such as EM, F1 or BLEU miss semantic equivalence. LLM-as-a-Judge setups based on commercial APIs are slow and expensive and rarely explain why an answer failed. HiEval addresses both problems with two models trained in sequence… ## Co-authors here 6 co-authors with a page, the most papers together first. - [Dongyan Zhao](https://md.research.elamur.ai/free/en/authors/dongyan-zhao/) - [Kai He](https://md.research.elamur.ai/free/en/authors/kai-he/) - [Qian Ma](https://md.research.elamur.ai/free/en/authors/qian-ma/) - [Shuai Zhao](https://md.research.elamur.ai/free/en/authors/shuai-zhao/) - [Yao Ma](https://md.research.elamur.ai/free/en/authors/yao-ma/) - [Zhen Li](https://md.research.elamur.ai/free/en/authors/zhen-li/) ## 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/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×2 Updated 2 November 2026