IMRRF : Integrating Multi-Source Retrieval and Redundancy Filtering for LLM -based Fake News Detection (2025)

Abstract: The widespread use of social networks has significantly accelerated the dissemination of information but has also facilitated the rapid spread of fake news, leading to various negative consequences. Recently, with the emergence of large language models (LLMs), researchers have focused on leveraging LLMs for automated fake news detection. Unfortunately, many issues remain to be addressed. First, the evidence retrieved to verify given fake news is often insufficient, limiting the performance of LLMs when reasoning directly from this evidence. Additionally, the retrieved evidence frequently contains substantial redundant information, which can interfere with the LLMs’ judgment. To address these limitations, we propose a Multiple Knowledge Sources Retrieval and LLM Knowledge Conversion framework, which enriches the evidence available for claim verification. We also introduce a Redundant Information Filtering Strategy, which minimizes the influence of irrelevant information on the LLM reasoning process. Extensive experiments conducted on two challenging fact-checking datasets demonstrate that our proposed method outperforms state-of-the-art fact-checking baselines. Our code is available at https://github.com/quark233/IMRRF/tree/main.

## introduction with the rapid development of social networks, information exchange has become more convenient. however, the risk of fake news also raises concerns. fake news is often misleading, deceptive, and sometimes malicious. it not only misleads the public and causes confusion but also significantly influences public opinion. previous fake news detection methods primarily relied on manual checking, which is time-consuming. therefore, developing effective automated fake news detection methods is crucial for curbing the spread and ensuring the authenticity of information. early efforts for automatic detection of fake news @xcite @xcite @xcite mainly focused on analyzing news content and using deep learning methods to determine the authenticity of the news. although such methods are effective, they make predictions only rely on the internal knowledge of the used models. @xcite demonstrated that the incorporation of external news-related evidence could enhance detection performance and provide factual support. consequently, fact-checking based methods @xcite have been the mainstream for fake news detection. despite significant progress that has been made, training such detection models require massive labeled data, which is time-consuming and labor-intensive. in addition, such methods @xcite produce only one classification label without human-readable explanations, which makes it difficult for fact-checkers to understand the reasons for model prediction. recently, large language models (llms) have shown considerable potential cross various tasks @xcite . llms have pre-trained on extensive data, and leveraging the world knowledge of llms for fake news detection has excellent potential. on the one hand, it does not require massive labeled data on specific domain for training and with highly generalization capacity. on the other hand, benefit from the powerful text generation capability of llms, it can generate human-readable explanations @xcite , which effectively enhanced human confidence in the detection results. caramancion (2023) directly prompting llms for claim 1 verification, but such a straightforward approach fails to engage the model making detailed reasoning process, resulting in limited capabilities. to enhance the reasoning performance, @xcite @xcite involve llms to decompose claims into simpler sub-claims or sub-questions, and then retrieve external evidences based on these decomposed results. such step-by-step reasoning process further improved the verification accuracy. although these methods effectively utilize llms for explainable fake news detection, two key challenges remain: 1) the retrieved evidences are often insufficient. existing methods depend on single knowledge source for evidence retrieval, such as specific corpus or online apis, which will result in incomplete evidence and limit the model's ability to verify claims comprehensively, as illustrated in figure 1 . to address these challenges, we propose imrrf. for the first challenge, we aim for the llm to provide more knowledge and obtain more comprehensive information through knowledge graph retrieval. for the second challenge, imrrf bypasses claim decomposition, directly retrieves external evidence from a targeted corpus and refines evidence containing redundant information under the guidance of knowledge graph retrieval results. finally, we combine the externally retrieved evidence with the supplementary evidence generated by the llm to verify complex claims. experiments on two real-world datasets show that imrrf significantly 1 the news to be verified. outperforms existing methods. our contributions are as follows: ## methodology in this paper, we aim to better integrate llm with fake news detection tasks, producing both final predictions and human-readable explanations. given a claim c, the summary of external retrieval evidence retrieved from multiple knowledge sources s and the supplementary evidence l generated by llm, we expect imrrf to produce a label y p ⊆ {supports, ref utes} and human-readable explanation y e . formally, this is expressed as: as illustrated in fig 2 , we propose a framework that retrieves comprehensive evidence based on claims from multiple knowledge sources. furthermore, we summarize the evidence to eliminate irrelevant information and use this refined evidence to guide the llm in converting its extensive internal world knowledge into supplementary evidence. ultimately, we combine the summarized evidence with the supplementary evidence to generate prediction labels and human-readable explanations. the whole process consists of four stages: multiple knowledge sources retrieval, redundant information filtering, llm world knowledge conversion, and claim verification. ## experiments 4.1 setting dataset. to assess the effectiveness of imrrf in detecting complex claims, we evaluate its performance on the feverous @xcite and hover @xcite datasets. compared to common fact-checking datasets, these datasets feature more intricate claims that requires multistep evidence retrieval and reasoning for validation. the feverous dataset is designed to validate complex claims involving both structured and unstructured data, with annotations based on sentence and table evidence from wikipedia. in our experiments, following programfc @xcite , we focus exclusively on claims requiring sentencebased evidence, which results in a subset of 2,962 claims. the hover dataset emphasizes multi-hop fact verification, covering claims that necessitate information integration and logical inference across multiple wikipedia entries. for our experiments, we utilize its validation set, which consists of 1,126 2-hop claims, 1,835 3-hop claims, and 1,039 4-hop claims. baseline. we compare the proposed method with the following baselines. programfc @xcite introduces an approach that enables llms to adaptively decompose complex claims into multi-step reasoning processes, progressively retrieving external evidence to support inference. hiss @xcite employs llms to decompose claims into simpler sub-claims, verifying them using the llms' internal knowledge when the model is confident in its verification; otherwise, it integrates evidence from online sources to verify the sub-claims. folk @xcite employs llms to decompose claims into first-order logic clauses consisting of predicates, enabling the decomposition of more effective sub-claims, retrieving supporting evidence from the web, and verifying each sub-claim independently. cok @xcite utilizes the cot strategy to generate initial inferences for knowledge-intensive problems, employing an adaptive query generator to construct structured queries for different databases and knowledge bases, and iteratively refining its reasoning steps based on the retrieved results to derive the final answer. implementation details. during the external evidence retrieval stage, we utilize gpt-3.5-turbo foot_0 to process intermediate steps efficiently. given its larger parameter scale and enhanced reasoning capabilities, gpt-4o 2 is employed in redundant information filtering and llm world knowledge conversion steps. for the claim verification phase, we employ flan-t5-xl foot_1 and gpt-4o. throughout all experiments, the temperature parameter for all llms is set to 0 and the full prompt used can be found in the appendix a. ## conclusion this paper proposes imrrf, which retrieves comprehensive evidence based on claims from multiple knowledge sources. in the retrieval process, im-rrf integrates specific textual corpus retrieval with knowledge graph retrieval to obtain more comprehensive evidence. subsequently, the model summarizes the retrieved external evidence and filters out irrelevant information to ensure the reliability of the evidence. using the summarized evidence, the model guides the llm to leverage its extensive internal world knowledge to generate supplementary evidence, thereby verifying the claims more effectively. imrrf demonstrates promising performance on the hover and feverous datasets, and we validate its effectiveness across different llms, with results demonstrating its superiority. additionally, we examine the quality of supplementary evidence generated by different llms. we also conduct experiments on prompt sensitivity and analyze error cases. our research introduces a novel detection method for fact-checking and contributes to the development of more effective verification approaches.