Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts

TL;DR

This paper introduces MetRag, a multi-layered thought framework for retrieval augmented generation that combines similarity, utility, and summarization to improve knowledge-intensive task performance beyond traditional similarity-based methods.

Contribution

The paper proposes a novel multi-layered thought approach for RAG, integrating utility models and summarization to enhance retrieval and generation quality.

Findings

MetRag outperforms existing retrieval augmented models on knowledge-intensive tasks.

Combining similarity and utility thoughts improves retrieval relevance.

Summarization of retrieved documents enhances generation compactness.

Abstract

In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmented models typically use similarity as a bridge between queries and documents and follow a retrieve then read procedure. In this work, we argue that similarity is not always the panacea and totally relying on similarity would sometimes degrade the performance of retrieval augmented generation. To this end, we propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented Generation framework. To begin with, beyond existing similarity oriented thought, we embrace a small scale utility model that draws supervision from an LLM for utility oriented thought and further come up with a smarter model by comprehensively combining the similarity and utility oriented thoughts. Furthermore, given the fact that the retrieved document set tends to be huge and using them in isolation makes it difficult to capture the commonalities and characteristics among them, we propose to make an LLM as a task adaptive summarizer to endow retrieval augmented generation with compactness-oriented thought. Finally, with multi layered thoughts from the precedent stages, an LLM is called for knowledge augmented generation. Extensive experiments on knowledge-intensive tasks have demonstrated the superiority of MetRag.