Title: Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation
Primary area: generative models
Keywords: retrieval-augmented generation, domain adaptation, information extraction

Abstract: Retrieval Augmented Generation (RAG) frameworks have shown significant promise in leveraging external knowledge to enhance the performance of large language models (LLMs). However, conventional RAG methods often retrieve documents based solely on surface-level relevance, leading to many issues: they may overlook deeply buried information within individual documents, miss relevant insights spanning multiple documents, and struggle to support tasks beyond traditional question answering without significant additional customization.   In this paper, we propose Insight-RAG, a novel framework designed to address these issues. In the initial stage of Insight-RAG, instead of using traditional retrieval methods, we employ an LLM to analyze the input query and task, extracting the underlying informational requirements. In the subsequent stage, a specialized LLM---trained on the document database---is queried to mine content that directly addresses these identified insights. Finally, by integrating the original query with the retrieved insights, similar to conventional RAG approaches, we employ a final LLM to generate a contextually enriched and accurate response. Using two scientific paper datasets, we created evaluation benchmarks targeting each of the mentioned issues and assessed Insight-RAG against traditional RAG pipeline.  Our results demonstrate that the Insight-RAG pipeline successfully addresses these challenges, outperforming existing methods by up to 60 percentage points. Supported by our comprehensive ablation studies---including the performance of each component and the quality of the identified insights---these findings suggest that integrating insight-driven retrieval within the RAG framework not only enhances performance but also broadens its applicability to tasks beyond conventional question answering.  We will release our dataset and code.

TL;DR: We introduce Insight-RAG, a novel framework that improves traditional RAG by adding an intermediate insight extraction step, significantly outperforming standard RAG on our newly created benchmarks targeting RAG's key limitations.