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Ruihui Hou

Ruihui Hou has 2 papers in our library, in 2026, most of them on Language models & pretraining, Dialogue systems; often with Tong Ruan, ChunMing Wang, Guangya Yu.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 2 · 22 works · 21 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13879

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

🎓East China University of Science and TechnologyShanghai · China2024–2026 · 16 paperscurrent🎓Henan UniversityKaifeng · China2020–2022 · 3 papers

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 12The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 1 workArts and Humanities · 1 workChemistry · 1 workComputational Theory and Mathematics · 1 workComputer Vision and Pattern Recognition · 1 workHealth Informatics · 1 workLanguage and Linguistics · 1 workMaterials Chemistry · 1 workMaterials Science · 1 workMedicine · 1 workPhysical and Theoretical Chemistry · 1 work

🧭 Topics 2Research subjects and languages the work is about.Dialogue systems · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue (2026)
    LLMs used as doctor simulators tend to reason in one of two weak ways. Fixed, expert-written reasoning templates are rigid. Long test-time-scaling chains, as in R1-style models, are wasteful. This paper adapts the idea of reusable "atomic thoughts" (small, composable reasoning units) to medical dialogue.
  2. MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026)
    Large language models could make molecular property prediction (MPP) explainable. However, they have trouble connecting molecular structure to activity. Earlier methods that supply structural hints also tend to treat chance co-occurrences in training data as chemical laws. MCLE-Mol addresses this with a framework…

 Co-authors here

8 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.

Updated

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