Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models

• URL: http://arxiv.org/abs/2410.13192v2
• Date: Sat, 14 Dec 2024 12:12:01 GMT
• Title: Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models
• Authors: Jiatao Li, Xinyu Hu, Xunjian Yin, Xiaojun Wan,
• Abstract summary: We investigate the overall effectiveness of Self-Docs, identifying key factors that shape their contribution to RAG performance.<n>Building on these insights, we develop a taxonomy grounded in Systemic Functional Linguistics to compare the influence of various Self-Docs categories.<n>Our findings reveal which types of Self-Docs are most beneficial and offer practical guidelines for leveraging them.
• Score: 39.243030042003646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of documents generated by LLMs themselves (Self-Docs) alongside retrieved documents has emerged as a promising strategy for retrieval-augmented generation systems. However, previous research primarily focuses on optimizing the use of Self-Docs, with their inherent properties remaining underexplored. To bridge this gap, we first investigate the overall effectiveness of Self-Docs, identifying key factors that shape their contribution to RAG performance (RQ1). Building on these insights, we develop a taxonomy grounded in Systemic Functional Linguistics to compare the influence of various Self-Docs categories (RQ2) and explore strategies for combining them with external sources (RQ3). Our findings reveal which types of Self-Docs are most beneficial and offer practical guidelines for leveraging them to achieve significant improvements in knowledge-intensive question answering tasks.

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