From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on RAG systems

Youngjoon Jang, Seongtae Hong, Junyoung Son, Sungjin Park, Chanjun Park, Heuiseok Lim

Keywords: Discourse and Pragmatics, Information Retrieval and Text Mining, Semantics: Lexical and Sentence-Level

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large language models (LLMs). However, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents, which can introduce ambiguity and interfere with in-context learning. In this study, we systematically investigate how entity coreference affects both document retrieval and generative performance in RAG-based systems, focusing on retrieval relevance, contextual understanding, and overall response quality. We demonstrate that coreference resolution enhances retrieval effectiveness and improves question-answering (QA) performance. Through comparative analysis of different pooling strategies in retrieval tasks, we find that mean pooling demonstrates superior context capturing ability after applying coreference resolution. In QA tasks, we discover that smaller models show greater improvement from the disambiguation process, likely due to their limited inherent capacity for handling referential ambiguity. With these findings, this study aims to provide a deeper understanding of the challenges posed by coreferential complexity in RAG, offering guidance for improving retrieval and generation in knowledge-intensive AI applications.

Archival Status: Archival

Paper Length: Short Paper (up to 4 pages of content)

Submission Number: 88