Elamur Research⌕
☰
home / authors / Chin-Chia Michael Yeh

Chin-Chia Michael Yeh

Chin-Chia Michael Yeh has 2 papers in our library, in 2025–2026, most of them on Information retrieval & search; often with Mahashweta Das, Menghai Pan, Yan Zheng.

2 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 24 · 104 works · 3,510 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 27 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8646

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

🏢VisaFoster City · United States2020–2026 · 51 paperscurrent🏢VisaLondon · United Kingdom2020–2026 · 18 paperscurrent🎓University of California, RiversideRiverside · United States2016–2023 · 18 papers🔬Academia SinicaTaipei · Taiwan2012–2014 · 8 papers🔬Research Center for Information Technology Innovation, Academia SinicaTaipei · Taiwan2012–2014 · 7 papers🎓University of UtahSalt Lake City · United States2021–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025 · 65 citations)
    Retrieval-augmented generation can be undermined by retrieved passages that are off-topic or misleading. Such passages lower answer quality and add inference cost. MAIN-RAG is a post-retrieval filtering framework that needs no training or fine-tuning and runs on top of a standard retriever. It uses three LLM agents:
  2. Demystify the Role of Memory in Machine Learning Engineering Agents (2026)
    LLM agents that automate machine learning engineering (MLE) often fall into the same errors repeatedly, both inside one search tree and across separate runs of a task.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, et al.2025 · 65 citations
  2. Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetHuiyuan Chen et al.2021 · 60 citations
  3. Demystify the Role of Memory in Machine Learning Engineering AgentsFindings of the Association for Computational LinguisticsXinyu Zhao, Junpeng Wang, Yuzhong Chen, et al.2026

 Co-authors here

10 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 27 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/chin-chia-michael-yeh/