Multi-Modal Fact-Verification Framework for Reducing Hallucinations in Large Language Models
This addresses the critical issue of false information generation in LLMs for real-world applications like healthcare and finance, offering a practical solution to improve trustworthiness.
The paper tackled the problem of hallucinations in Large Language Models by developing a fact verification framework that cross-checks outputs against multiple knowledge sources, reducing hallucinations by 67% and achieving 89% satisfaction from domain experts.
While Large Language Models have transformed how we interact with AI systems, they suffer from a critical flaw: they confidently generate false information that sounds entirely plausible. This hallucination problem has become a major barrier to deploying these models in real-world applications where accuracy matters. We developed a fact verification framework that catches and corrects these errors in real-time by cross checking LLM outputs against multiple knowledge sources. Our system combines structured databases, live web searches, and academic literature to verify factual claims as they're generated. When we detect inconsistencies, we automatically correct them while preserving the natural flow of the response. Testing across various domains showed we could reduce hallucinations by 67% without sacrificing response quality. Domain experts in healthcare, finance, and scientific research rated our corrected outputs 89% satisfactory a significant improvement over unverified LLM responses. This work offers a practical solution for making LLMs more trustworthy in applications where getting facts wrong isn't an option.