mlsanityData to 14 Oct 2026

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

arXiv 2410.13192 ↗First posted 17 Oct 2024cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 55 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

28 of 55 references.

References · 28

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16 more
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