Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models
Jiatao Li, Xinyu Hu, Xunjian Yin, Xiaojun Wan
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.
BibTeX
@inproceedings{li-etal-2025-evaluating,
title = "Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models",
author = "Li, Jiatao and
Hu, Xinyu and
Yin, Xunjian and
Wan, Xiaojun",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.149/",
pages = "2741--2775",
ISBN = "979-8-89176-195-7"
}