A Multi-Source Retrieval Question Answering Framework Based on RAG

TL;DR

This paper introduces MSRAG, a multi-source retrieval framework combining GPT-3.5 and web retrieval to improve the accuracy and reliability of retrieval-augmented question answering systems.

Contribution

It proposes a novel multi-source retrieval framework that leverages GPT-3.5 and web retrieval to reduce noise and improve QA performance.

Findings

MSRAG outperforms existing RAG frameworks in accuracy.

The framework enhances retrieval relevance and system efficiency.

Experiments show improved results on knowledge-intensive QA datasets.

Abstract

With the rapid development of large-scale language models, Retrieval-Augmented Generation (RAG) has been widely adopted. However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study proposes a method that replaces traditional retrievers with GPT-3.5, leveraging its vast corpus knowledge to generate retrieval information. We also propose a web retrieval based method to implement fine-grained knowledge retrieval, Utilizing the powerful reasoning capability of GPT-3.5 to realize semantic partitioning of problem.In order to mitigate the illusion of GPT retrieval and reduce noise in Web retrieval,we proposes a multi-source retrieval framework, named MSRAG, which combines GPT retrieval with web retrieval. Experiments on multiple knowledge-intensive QA datasets demonstrate that the proposed framework in this study performs better than existing RAG framework in enhancing the overall efficiency and accuracy of QA systems.