🤖 AI Summary
Large language models (LLMs) frequently generate hallucinated content and lack verifiable citations when producing fact-intensive text. To address this, we propose a multi-stage self-verification framework that orchestrates a sequential pipeline of *fact verification → reflective revision → citation integration*. The method synergistically combines chain-of-thought (CoT) reasoning with dual knowledge validation—leveraging both internal consistency checks and external authoritative source alignment—to dynamically perform fine-grained factual scrutiny during generation. When inconsistencies are detected, the model triggers reflective revision and automatically annotates traceable, context-aligned citations. Compared to state-of-the-art approaches, our framework substantially reduces hallucination rates while improving factual accuracy and citation reliability. Empirical evaluation demonstrates its effectiveness in high-fidelity applications such as scientific writing and news generation, where trustworthiness and evidential grounding are critical.
📝 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.