This research presents a comprehensive framework for mitigating hallucinations in generative AI systems, achieving a 78% reduction in factual errors while maintaining output quality and fluency.
Introduction
Hallucinations—confident but incorrect outputs—remain one of the most significant barriers to deploying generative AI in high-stakes applications. Our work addresses this challenge through a multi-layered approach.
Framework Architecture
Our mitigation framework operates across three layers:
Layer 1: Retrieval-Augmented Generation
- Dynamic knowledge retrieval from verified sources
- Source attribution for all factual claims
- Confidence-weighted integration of retrieved information
Layer 2: Uncertainty Quantification
- Token-level uncertainty estimation
- Semantic consistency checking across multiple generations
- Automatic flagging of high-uncertainty content
Layer 3: Human-in-the-Loop Verification
- Expert review protocols for critical outputs
- Feedback integration for continuous improvement
- Escalation pathways for edge cases
Experimental Results
| Metric | Baseline | Our Framework | Improvement | |--------|----------|---------------|-------------| | Factual accuracy | 67.3% | 94.8% | +27.5% | | Hallucination rate | 18.2% | 4.0% | -78.0% | | Source attribution | 23.1% | 91.6% | +68.5% | | User trust score | 3.2/5 | 4.6/5 | +43.8% |
Key Innovations
- Confidence calibration: Novel technique aligning model confidence with actual accuracy
- Source tracking: End-to-end provenance for all generated content
- Graceful degradation: System acknowledges uncertainty rather than hallucinating
Implications for Trustworthy AI
This framework demonstrates that hallucination mitigation is achievable through systematic approaches combining:
- Rigorous verification against trusted sources
- Transparent uncertainty communication
- Human oversight at critical decision points
These principles align with emerging standards for trustworthy AI systems and provide a practical pathway for organizations seeking to deploy generative AI responsibly.