Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking
arXiv:2603.00267v1 Announce Type: new Abstract: Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving accurate and trustworthy evidence. Previous methods rely on semantic and social-contextual patterns learned from training data, which limits their generalization to new data distributions. Recently, Retrieval Augmented Generation (RAG) based methods have been proposed to utilize the reasoning capability of LLMs with retrieved grounding evidence documents. However, these methods largely rely on textual similarity for evidence retrieval and struggle to retrieve evidence that captures multi-hop semantic relations within rich document contents. These limitations lead to overlooking subtle factual correlations between the evidence and the claims to be fact-checked during evidence retrieval, thus causing inaccurate veracity predictions. To address these issues, we p
arXiv:2603.00267v1 Announce Type: new Abstract: Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving accurate and trustworthy evidence. Previous methods rely on semantic and social-contextual patterns learned from training data, which limits their generalization to new data distributions. Recently, Retrieval Augmented Generation (RAG) based methods have been proposed to utilize the reasoning capability of LLMs with retrieved grounding evidence documents. However, these methods largely rely on textual similarity for evidence retrieval and struggle to retrieve evidence that captures multi-hop semantic relations within rich document contents. These limitations lead to overlooking subtle factual correlations between the evidence and the claims to be fact-checked during evidence retrieval, thus causing inaccurate veracity predictions. To address these issues, we propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence. LLM-enabled retrieval is designed to assess the claims and retrieve the most relevant knowledge subgraphs, forming structured evidence for fact verification. To augment the knowledge graph evidence, we retrieve web contents for completion. The above process is implemented as an automatic Markov Decision Process (MDP): A reasoning LLM agent decides what actions to take according to the current evidence and the claims. To adapt the MDP for fact-checking, we use prompt optimization to fine-tune the agentic LLM.
Executive Summary
This article proposes WKGFC, a novel fact-checking approach that utilizes authorized open knowledge graphs and large language models (LLMs) to retrieve accurate and trustworthy evidence. The method combines LLM-enabled retrieval with the reasoning capability of LLMs to assess claims and retrieve relevant knowledge subgraphs for fact verification. To augment the knowledge graph evidence, web contents are retrieved for completion. The process is implemented as an automatic Markov Decision Process (MDP) with prompt optimization for fine-tuning the agentic LLM. The authors claim that WKGFC addresses limitations of existing methods, such as overlooking multi-hop semantic relations and subtle factual correlations. The proposed approach demonstrates potential for scalable and robust fact-checking, but its efficacy and generalizability require further evaluation.
Key Points
- ▸ WKGFC utilizes authorized open knowledge graphs and LLMs for accurate and trustworthy evidence retrieval
- ▸ The approach combines LLM-enabled retrieval with LLM reasoning to assess claims and retrieve relevant knowledge subgraphs
- ▸ Web contents are retrieved for completion to augment knowledge graph evidence
Merits
Strength in Addressing Limitations
WKGFC addresses the limitations of existing methods in overlooking multi-hop semantic relations and subtle factual correlations, offering a more comprehensive approach to fact-checking
Potential for Scalability and Robustness
The proposed approach demonstrates potential for scalable and robust fact-checking, which is crucial for addressing the spread of misinformation
Demerits
Complexity and Computational Requirements
The use of LLMs and knowledge graphs may introduce complexity and high computational requirements, which can be a limitation in real-world applications
Dependence on High-Quality Training Data
The performance of WKGFC may be dependent on the quality of the training data used to train the LLMs, which can be a limitation in scenarios where high-quality training data is not available
Expert Commentary
The proposed approach of WKGFC is a promising direction for fact-checking research, as it leverages the strengths of both knowledge graphs and LLMs. However, further evaluation is necessary to assess the efficacy and generalizability of WKGFC in real-world scenarios. Additionally, the complexity and computational requirements of the approach may be a limitation in certain contexts. Nevertheless, the potential for scalability and robustness makes WKGFC an attractive option for fact-checking applications.
Recommendations
- ✓ Further evaluation of WKGFC in real-world scenarios is necessary to assess its efficacy and generalizability
- ✓ Investment in AI and ML research and development is necessary to enhance fact-checking capabilities
