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        <title>Retrieval-Augmented Generation with Estimation of Source Reliability</title>
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        <namePart type="given">Jeongyeon</namePart>
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        <namePart type="given">Junyoung</namePart>
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            <namePart type="given">Tanmoy</namePart>
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    <abstract>Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs) by retrieving information from external databases, which are typically composed of diverse sources, to supplement the limited internal knowledge of LLMs. However, the standard RAG often risks retrieving incorrect information, as it relies solely on relevance between a query and a document, overlooking the heterogeneous reliability of these sources. To address this issue, we propose Reliability-Aware RAG (RA-RAG), a new multi-source RAG framework that estimates the reliability of sources and leverages this information to prioritize highly reliable and relevant documents, ensuring more robust and accurate response generation. Specifically, RA-RAG first estimates source reliability by cross-checking information across multiple sources. It then retrieves documents from the top-ąppa reliable and relevant sources and aggregates their information using weighted majority voting (WMV), where the selective retrieval ensures scalability while not compromising the performance. Comprehensive experiments show that RA-RAG consistently outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. Furthermore, we demonstrate the ability of RA-RAG to estimate real-world sources’ reliability, highlighting its practical applicability. Our code and data are available at RA-RAG.</abstract>
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%0 Conference Proceedings
%T Retrieval-Augmented Generation with Estimation of Source Reliability
%A Hwang, Jeongyeon
%A Park, Junyoung
%A Park, Hyejin
%A Kim, Dongwoo
%A Park, Sangdon
%A Ok, Jungseul
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F hwang-etal-2025-retrieval
%X Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs) by retrieving information from external databases, which are typically composed of diverse sources, to supplement the limited internal knowledge of LLMs. However, the standard RAG often risks retrieving incorrect information, as it relies solely on relevance between a query and a document, overlooking the heterogeneous reliability of these sources. To address this issue, we propose Reliability-Aware RAG (RA-RAG), a new multi-source RAG framework that estimates the reliability of sources and leverages this information to prioritize highly reliable and relevant documents, ensuring more robust and accurate response generation. Specifically, RA-RAG first estimates source reliability by cross-checking information across multiple sources. It then retrieves documents from the top-ąppa reliable and relevant sources and aggregates their information using weighted majority voting (WMV), where the selective retrieval ensures scalability while not compromising the performance. Comprehensive experiments show that RA-RAG consistently outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. Furthermore, we demonstrate the ability of RA-RAG to estimate real-world sources’ reliability, highlighting its practical applicability. Our code and data are available at RA-RAG.
%U https://aclanthology.org/2025.emnlp-main.1738/
%P 34267-34291

Markdown (Informal)

[Retrieval-Augmented Generation with Estimation of Source Reliability](https://aclanthology.org/2025.emnlp-main.1738/) (Hwang et al., EMNLP 2025)

• Retrieval-Augmented Generation with Estimation of Source Reliability (Hwang et al., EMNLP 2025)

ACL

• Jeongyeon Hwang, Junyoung Park, Hyejin Park, Dongwoo Kim, Sangdon Park, and Jungseul Ok. 2025. Retrieval-Augmented Generation with Estimation of Source Reliability. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 34267–34291, Suzhou, China. Association for Computational Linguistics.