Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation
Published in Arxiv, 2025
This paper presents EDC²-RAG, a retrieval-augmented generation framework that improves LLM inference by dynamically clustering retrieved documents to remove noise, redundancy, and irrelevant content. Built on GPT-3.5, EDC²-RAG demonstrates robust performance across knowledge-QA and hallucination detection tasks.
Recommended citation: Weitao Li, Kaiming Liu, Xiangyu Zhang, Xuanyu Lei, Weizhi Ma, Yang Liu, "Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation", arXiv:2504.03165 [cs.CL]
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