Coreference Resolution Boosts RAG Performance in NLP
A recent study published on arXiv (2507.07847) explores the role of coreference resolution in enhancing Retrieval-Augmented Generation (RAG) systems. By combining external document retrieval with large language models (LLMs), RAG aims to improve factual accuracy and minimize hallucinations. However, ambiguities in coreference within retrieved documents can hinder in-context learning. This research thoroughly analyzes how entity coreference affects both document retrieval and generative capabilities, emphasizing retrieval relevance, contextual comprehension, and the quality of responses. Findings indicate that coreference resolution significantly boosts retrieval efficiency and question-answering (QA) outcomes. Additionally, a comparative assessment of pooling methods shows that mean pooling excels in capturing context post-coreference resolution.
Key facts
- arXiv paper 2507.07847 examines coreference resolution in RAG systems.
- RAG improves factual consistency and reduces hallucinations in LLMs.
- Coreferential ambiguity in retrieved documents hinders RAG performance.
- Coreference resolution enhances retrieval effectiveness and QA performance.
- Mean pooling shows superior context capturing after coreference resolution.
- The study focuses on retrieval relevance, contextual understanding, and response quality.
- RAG integrates external document retrieval with large language models.
- The research systematically investigates entity coreference's impact.
Entities
Institutions
- arXiv