Papers by Yiruo Cheng
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation (2025.findings-naacl)
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Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing research focuses on single-turn RAG, leaving a gap in addressing multi-turn conversations . a new benchmark is designed to assess RAG systems in realistic multi-turned conversations based on Wikipedia . |
| Approach: | They propose a large-scale benchmark to assess RAG systems in multi-turn contexts . CORAL includes diverse information-seeking conversations automatically derived from Wikipedia . authors propose unified framework to standardize various conversational RAG methods . |
| Outcome: | The proposed framework supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. |
Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding (2024.acl-long)
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| Challenge: | Conversational dense retrieval models lack interpretability, hindering intuitive understanding of model behaviors . a major limitation of conversational dense search is their lack of interpretability . |
| Approach: | They propose to transform opaque session embeddings into explicit interpretable text . they propose to incorporate external interpretable query rewrites into the transformation process . |
| Outcome: | The proposed approach yields more interpretable text and preserves original retrieval performance over baselines. |