In Defense of RAG in the Era of Long-Context Language Models

• URL: http://arxiv.org/abs/2409.01666v1
• Date: Tue, 3 Sep 2024 07:17:41 GMT
• Title: In Defense of RAG in the Era of Long-Context Language Models
• Authors: Tan Yu, Anbang Xu, Rama Akkiraju,
• Abstract summary: Retrieval-augmented generation (RAG) has been a reliable solution for context-based answer generation in the past. Recent studies show that long-context LLMs significantly outperform RAG in long-context applications. We propose an order-preserve retrieval-augmented generation (OP-RAG) mechanism, which significantly improves the performance of RAG for long-context question-answer applications.
• Score: 17.397639724806364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Overcoming the limited context limitations in early-generation LLMs, retrieval-augmented generation (RAG) has been a reliable solution for context-based answer generation in the past. Recently, the emergence of long-context LLMs allows the models to incorporate much longer text sequences, making RAG less attractive. Recent studies show that long-context LLMs significantly outperform RAG in long-context applications. Unlike the existing works favoring the long-context LLM over RAG, we argue that the extremely long context in LLMs suffers from a diminished focus on relevant information and leads to potential degradation in answer quality. This paper revisits the RAG in long-context answer generation. We propose an order-preserve retrieval-augmented generation (OP-RAG) mechanism, which significantly improves the performance of RAG for long-context question-answer applications. With OP-RAG, as the number of retrieved chunks increases, the answer quality initially rises, and then declines, forming an inverted U-shaped curve. There exist sweet points where OP-RAG could achieve higher answer quality with much less tokens than long-context LLM taking the whole context as input. Extensive experiments on public benchmark demonstrate the superiority of our OP-RAG.

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