Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2502.13019v2
• Date: Thu, 20 Feb 2025 16:47:42 GMT
• Title: Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation
• Authors: Sha Li, Naren Ramakrishnan,
• Abstract summary: Large Language Models (LLMs) remain vulnerable to hallucinations due to their limited parametric knowledge and lack of domain-specific expertise.<n>Retrieval-Augmented Generation (RAG) addresses this challenge by incorporating external document retrieval to augment the knowledge base of LLMs.<n>We introduce a compact, efficient, and pluggable module designed to refine external knowledge sources before feeding them to the generator.
• Score: 28.568010424711563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the remarkable capabilities of Large Language Models (LLMs) in various NLP tasks, they remain vulnerable to hallucinations due to their limited parametric knowledge and lack of domain-specific expertise. Retrieval-Augmented Generation (RAG) addresses this challenge by incorporating external document retrieval to augment the knowledge base of LLMs. In this approach, RAG retrieves document chunks from an external corpus in response to a query, which are then used as context for the downstream language model to generate an answer. However, these retrieved knowledge sources often include irrelevant or erroneous information, undermining the effectiveness of RAG in downstream tasks. To overcome this limitation, we introduce a compact, efficient, and pluggable module designed to refine external knowledge sources before feeding them to the generator. The module reconstructs retrieved content by extracting the most relevant and supportive information and reorganising it into a concise, query-specific format. Through a three-stage training paradigm - comprising supervised fine-tuning, contrastive multi-task learning, and reinforcement learning-based alignment - it prioritises critical knowledge and aligns it with the generator's preferences. This method enables LLMs to produce outputs that are more accurate, reliable, and contextually appropriate.

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