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

In retrieval-augmented generation systems, the integration of self-generated documents (SGDs) alongside retrieved content has emerged as a promising strategy for enhancing the performance of large language model. However, previous research primarily focuses on optimizing the use of SGDs, with the inherent properties of SGDs remaining underexplored. Therefore, this paper conducts a comprehensive analysis of different types of SGDs and experiments on various knowledge-intensive tasks. We develop a taxonomy of SGDs grounded in Systemic Functional Linguistics (SFL) to compare the influence of different SGD categories. Our findings offer key insights into what kinds of SGDs most effectively contribute to improving LLM's performance. The results and further fusion methods based on SGD categories also provide practical guidelines for taking better advantage of SGDs to achieve significant advancements in knowledge-driven QA tasks with RAG.

SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval

Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integration of external information. However, conventional RAG approaches, which prioritize top-ranked documents based solely on query-context relevance, often introduce redundancy and conflicting information. This issue is particularly evident in unsupervised retrieval settings, where there are no mechanisms to effectively mitigate these problems, leading to suboptimal context selection. To address this, we propose Selection using Matrices for Augmented Retrieval (SMART) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG. SMART leverages Determinantal Point Processes (DPPs) to simultaneously model relevance, diversity and conflict, ensuring the selection of potentially high-quality contexts. Experimental results across multiple datasets demonstrate that SMART significantly enhances QA performance and surpasses previous unsupervised context selection methods, showing a promising strategy for RAG.