--- title: "Hui Huang · Shenzhen University — author profile and papers" description: "Hui Huang, Shenzhen University: 4 papers on Elamur Research (2024–2026), h-index 0 — affiliations, topics, co-authors and who cites them…" canonical: "https://research.elamur.ai/free/en/authors/hui-huang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hui-huang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Hui Huang # Hui Huang Hui Huang has 4 papers in our library, in 2024–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Jiaheng Liu](https://md.research.elamur.ai/free/en/authors/jiaheng-liu/), [Shilong Li](https://md.research.elamur.ai/free/en/authors/shilong-li/), [Weiqin Wang](https://md.research.elamur.ai/free/en/authors/weiqin-wang/). 4 papers in our library · 2024–2026 [OpenAlex](https://openalex.org/A5139824359) · [**h-index 0**](https://openalex.org/authors/A5139824359) · [5 works](https://openalex.org/works?filter=author.id:A5139824359) · [0 citations](https://openalex.org/works?filter=author.id:A5139824359&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/hui-huang/#papers) · [🔗 Cited 41 times in our library](https://md.research.elamur.ai/free/en/authors/hui-huang/cited-by/) · [⭐ Paper Stars ranking: #8028](https://md.research.elamur.ai/free/en/paper-stars/page/81/#star-8028) 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=Hui+Huang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Hui+Huang) · [dblp](https://dblp.org/search?q=Hui+Huang) · [ACL Anthology](https://aclanthology.org/search/?q=Hui+Huang) ## Affiliations [🎓**Shenzhen University** Shenzhen · China 2026 · 3 papers*current*](https://md.research.elamur.ai/free/en/universities/shenzhen-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 · 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/) · [Language and Linguistics · 4 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 1 Research subjects and languages the work is about.[Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/hui-huang/year/2026/) · [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/hui-huang/year/2025/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/hui-huang/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models](https://md.research.elamur.ai/free/en/papers/he-2024-chinese-simpleqa-chinese-factuality/)** *(2024 · 34 citations)* Hallucination remains a major weakness of large language models. Factuality is hard to measure when models produce long answers that mix many claims. OpenAI's SimpleQA addressed this with short fact-seeking questions, but it covers only English. Chinese SimpleQA is a Chinese counterpart: 3,000 short questions, each… 2. **[Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/)** *(2026 · 4 citations)* Test-time reinforcement learning (TTRL) trains LLMs on reasoning tasks without gold answers. It samples many responses and treats the majority-voted answer as a pseudo-label. This has two weaknesses. Every vote counts equally, so a popular but wrong answer gets reinforced (confirmation bias), and the binary match… 3. **[MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training](https://md.research.elamur.ai/free/en/papers/huang-2025-musc-improving-complex-instruction-following/)** *(2025 · 4 citations)* Large language models often fail on instructions that combine many constraints, frequently dropping some of them. 4. **[AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models](https://md.research.elamur.ai/free/en/papers/shen-2026-anchormem-anchored-facts-associative-contexts/)** *(2026)* LLM memory systems such as A-Mem and Mem0 repeatedly summarize and rewrite past interactions. This can wash out fine details. Entity-graph indexing, as in HippoRAG 2, can instead link unrelated memories through common, generic entities. AnchorMem addresses both problems by separating what is used for retrieval from… ## Co-authors here 12 of 18 co-authors with a page, the most papers together first. - [Jiaheng Liu](https://md.research.elamur.ai/free/en/authors/jiaheng-liu/) ×2 - [Shilong Li](https://md.research.elamur.ai/free/en/authors/shilong-li/) ×2 - [Weiqin Wang](https://md.research.elamur.ai/free/en/authors/weiqin-wang/) ×2 - [Yancheng He](https://md.research.elamur.ai/free/en/authors/yancheng-he/) ×2 - [Yile Wang](https://md.research.elamur.ai/free/en/authors/yile-wang/) ×2 - [Bing Xu](https://md.research.elamur.ai/free/en/authors/bing-xu/) - [Boren Zheng](https://md.research.elamur.ai/free/en/authors/boren-zheng/) - [Conghui Zhu](https://md.research.elamur.ai/free/en/authors/conghui-zhu/) - [Hangyu Guo](https://md.research.elamur.ai/free/en/authors/hangyu-guo/) - [Muyun Yang](https://md.research.elamur.ai/free/en/authors/muyun-yang/) - [Sijie Cheng](https://md.research.elamur.ai/free/en/authors/sijie-cheng/) - [Tiejun Zhao](https://md.research.elamur.ai/free/en/authors/tiejun-zhao/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/), [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ## Cited by Papers of our library cite their works 41 times (of the 149,439 papers whose bibliographies we read); [17 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/hui-huang/cited-by/). Updated 1 November 2026