Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
arXiv:2511.03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking approach that leverages the individual strengths of each component. Our system comprises three autonomous steps: 1) a Knowledge Graph (KG) Retrieval for rapid one-hop l
The rapid advancement and widespread deployment of Large Language Models necessitate robust fact-checking mechanisms as concerns around misinformation and 'hallucinations' escalate.
This development addresses a critical weakness in current AI systems, moving towards more reliable and verifiable AI-generated information, which is essential for trust and widespread adoption.
Fact-checking shifts from being solely human-driven or limited to single AI modalities, integrating diverse AI capabilities for more accurate and interpretable verification processes.
- · Fact-checking platforms
- · AI ethics and safety researchers
- · Knowledge graph developers
- · Consumers of information
- · Standalone LLM-based fact-checkers
- · Producers of misinformation
- · Platforms with weak content moderation
More accurate and interpretable AI-driven fact-checking becomes widely available, improving information veracity.
Reduced spread of misinformation and 'hallucinated' content generated by LLMs, bolstering public trust in AI applications.
The development of 'explainable AI' fact-checking systems could set new industry standards for transparency and accountability in AI, influencing regulatory frameworks.
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Read at arXiv cs.AI