  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.
