Elamur Research⌕
☰
home / authors / Hui Huang

Hui Huang

Hui Huang has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Jiaheng Liu, Shilong Li, Weiqin Wang.

4 papers in our library · 2024–2026

OpenAlex · h-index 0 · 5 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 41 times in our library · ⭐ Paper Stars ranking: #8028

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

🎓Shenzhen UniversityShenzhen · China2026 · 3 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20262 papers20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (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 (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 (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 (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.

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 41 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/hui-huang/