Papers by Chin-Chia Michael Yeh
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025.acl-long)
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Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou
| Challenge: | Existing RAG systems struggle with the quality of retrieval documents, causing performance degradation and reducing performance. |
| Approach: | They propose a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents. |
| Outcome: | The proposed framework outperforms existing RAG frameworks in QA benchmarks and shows superior answer consistency and answer accuracy over baseline methods. |
Enhancing Foundation Models in Transaction Understanding with LLM-based Sentence Embeddings (2025.emnlp-industry)
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| Challenge: | Existing foundation models for tabular transactional data rely on index-based representations for categorical merchant fields. |
| Approach: | They propose a framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models. |
| Outcome: | The proposed framework improves performance on large transaction datasets. |
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)
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Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu
| Challenge: | Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights. |
| Approach: | They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. |
| Outcome: | The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. |
Demystify the Role of Memory in Machine Learning Engineering Agents (2026.findings-acl)
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Xinyu Zhao, Junpeng Wang, Yuzhong Chen, Menghai Pan, Chin-Chia Michael Yeh, Jiarui Sun, Yan Zheng, Mahashweta Das, Tianlong Chen
| Challenge: | Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors can inform future success. |
| Approach: | They propose a dynamic coding memory that captures and reuses debugging experiences and integrates it into two representative agent paradigms. |
| Outcome: | The proposed agent model captures and reuses debugging experiences and integrates it into two agent paradigms. |