MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation

arXiv 2508.03553 ↗First posted 5 Aug 2025cs.IR cs.CLcited by 3

On the map: Retrieval-augmented generation → Hallucination detection in RAG

Abstract (arXiv)

Retrieval Augmented Generation (RAG) has emerged as a promising solution to address hallucination issues in Large Language Models (LLMs). However, the integration of multiple retrieval sources, while potentially more informative, introduces new challenges that can paradoxically exacerbate hallucination problems. These challenges manifest primarily in two aspects: the sparse distribution of multi-source data that hinders the capture of logical relationships and the inherent inconsistencies among different sources that lead to information conflicts. To address these challenges, we propose MultiRAG, a novel framework designed to mitigate hallucination in multi-source retrieval-augmented generation through knowledge-guided approaches. Our framework introduces two key innovations: (1) a knowledge construction module that employs multi-source line graphs to efficiently aggregate logical relationships across different knowledge sources, effectively addressing the sparse data distribution issue; and (2) a sophisticated retrieval module that implements a multi-level confidence calculation mechanism, performing both graph-level and node-level assessments to identify and eliminate unreliable information nodes, thereby reducing hallucinations caused by inter-source inconsistencies. Extensive experiments on four multi-domain query datasets and two multi-hop QA datasets demonstrate that MultiRAG significantly enhances the reliability and efficiency of knowledge retrieval in complex multi-source scenarios. textcolorblueOur code is available in https://github.com/wuwenlong123/MultiRAG.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation. Builds on 36 of its 53 references that are in the map. First posted on arXiv 5 Aug 2025.

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36 of 53 references.

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2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models31 Dec

Other references · 35

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2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
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2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668

23 more

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2024Metacognitive Retrieval-Augmented Large Language Modelscited by 26
2024RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Modelscited by 25
2025U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystackcited by 20
2024Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrievalcited by 16
2024Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extractioncited by 14
2015Truth Finding on the Deep Web: Is the Problem Solved?cited by 10
2024Large Language Model Enhanced Knowledge Representation Learning: A Surveycited by 7
2024HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embeddingcited by 6
2023Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graphcited by 1

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