Retrieval-Augmented Generation with Estimation of Source Reliability

• URL: http://arxiv.org/abs/2410.22954v1
• Date: Wed, 30 Oct 2024 12:09:29 GMT
• Title: Retrieval-Augmented Generation with Estimation of Source Reliability
• Authors: Jeongyeon Hwang, Junyoung Park, Hyejin Park, Sangdon Park, Jungseul Ok,
• Abstract summary: Reliability-Aware RAG (RA-RAG) estimates the reliability of multiple sources and incorporates this information into both retrieval and aggregation processes. We introduce a benchmark designed to reflect real-world scenarios with heterogeneous source reliability.
• Score: 15.69681944254975
• License:
• Abstract: Retrieval-augmented generation (RAG) addresses key limitations of large language models (LLMs), such as hallucinations and outdated knowledge, by incorporating external databases. These databases typically consult multiple sources to encompass up-to-date and various information. However, standard RAG methods often overlook the heterogeneous source reliability in the multi-source database and retrieve documents solely based on relevance, making them prone to propagating misinformation. To address this, we propose Reliability-Aware RAG (RA-RAG) which estimates the reliability of multiple sources and incorporates this information into both retrieval and aggregation processes. Specifically, it iteratively estimates source reliability and true answers for a set of queries with no labelling. Then, it selectively retrieves relevant documents from a few of reliable sources and aggregates them using weighted majority voting, where the selective retrieval ensures scalability while not compromising the performance. We also introduce a benchmark designed to reflect real-world scenarios with heterogeneous source reliability and demonstrate the effectiveness of RA-RAG compared to a set of baselines.

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