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Multi-Modal Fact-Verification Framework for Reducing Hallucinations in Large Language Models

Piyushkumar Patel

TL;DR

This work tackles the pervasive hallucination problem in large language models by introducing a real-time, multi-modal fact-verification framework. It combines dynamic knowledge integration from structured graphs, live web sources, and scholarly literature with multi-source evidence validation and calibrated probabilistic scoring to detect and correct factual errors during inference while preserving fluency. Key contributions include dynamic knowledge integration, Bayesian evidence fusion, an adaptive correction pipeline, and a thorough experimental evaluation showing a 67% reduction in hallucinations and 89% expert-satisfaction in domain-specific tasks. The framework demonstrates significant practical impact for deploying trustworthy LLMs in healthcare, finance, and scientific domains, offering scalable, modular integration without retraining.

Abstract

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.

Multi-Modal Fact-Verification Framework for Reducing Hallucinations in Large Language Models

TL;DR

This work tackles the pervasive hallucination problem in large language models by introducing a real-time, multi-modal fact-verification framework. It combines dynamic knowledge integration from structured graphs, live web sources, and scholarly literature with multi-source evidence validation and calibrated probabilistic scoring to detect and correct factual errors during inference while preserving fluency. Key contributions include dynamic knowledge integration, Bayesian evidence fusion, an adaptive correction pipeline, and a thorough experimental evaluation showing a 67% reduction in hallucinations and 89% expert-satisfaction in domain-specific tasks. The framework demonstrates significant practical impact for deploying trustworthy LLMs in healthcare, finance, and scientific domains, offering scalable, modular integration without retraining.

Abstract

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.
Paper Structure (21 sections, 1 equation, 2 figures, 2 tables, 1 algorithm)

This paper contains 21 sections, 1 equation, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Hallucination Detection and Mitigation
  4. Knowledge-Grounded Text Generation
  5. Automated Fact-Checking Systems
  6. Uncertainty Quantification in Language Models
  7. Proposed Method
  8. Dynamic Knowledge Integration
  9. Multi-Source Evidence Validation
  10. Probabilistic Confidence Scoring
  11. Adaptive Correction Pipeline
  12. Experimental Evaluation
  13. Experimental Setup
  14. Metrics
  15. Results
  16. ...and 6 more sections

Figures (2)

  • Figure 1: Detailed system architecture showing technical implementation components. The framework includes specific technologies (Neo4j, Google API), algorithms (Bayesian aggregation), and mathematical formulations for confidence scoring.
  • Figure 2: Detailed technical implementation showing specific algorithms, query structures, timing constraints, and mathematical formulations. The example demonstrates real system parameters including confidence thresholds, API response times, and Bayesian fusion weights.