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October 3, 2025Open Access

Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification

Key Points

The introduction of VeriFact-CoT significantly enhances the factual accuracy of LLM outputs, reducing hallucination risks.
A multi-stage process integrates fact verification and citation, ensuring more reliable content generation in LLMs used for critical applications.
This approach systematically allows LLMs to review their reasoning, improving trustworthiness and traceability in outputs.
Applications span scientific research, legal consultations, and news reporting, highlighting the importance of credible citation in information generation.

Abstract

This research introduces VeriFact-CoT (Verified Factual Chain-of-Thought), a novel method designed to address the pervasive issues of hallucination and the absence of credible citation sources in Large Language Models (LLMs) when generating complex, fact-sensitive content. By incorporating a multi-stage mechanism of 'fact verification-reflection-citation integration,' VeriFact-CoT empowers LLMs to critically self-examine and revise their intermediate reasoning steps and final answers. This process significantly enhances the objective accuracy, trustworthiness, and traceability of the generated outputs, making LLMs more reliable for applications demanding high fidelity such as scientific research, news reporting, and legal consultation.

Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification

Key Points

Abstract

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