Hallucination Detector
Real-time Hallucination Detection for LLMs
6 followers
Real-time Hallucination Detection for LLMs
6 followers
Hallucination Detector is a system that goes beyond basic fact-checking, using statistical analysis and weighted scoring to identify false or unsupported outputs from LLMs. It provides real-time detection, confidence scoring, coverage across 50+ domains, and model performance tracking. Errors are classified by severity, and accuracy improves through statistical accumulation. With dual-axis scoring, precise risk weights, and trust dampening, it delivers higher reliability and AI transparency.
@said_zeghidi The risk weight constants are a smart touch, curious how they adapt across different industries
@masump Hi Parvej.
Industry-Specific Adaptations:
Healthcare: Higher weights (1.5x-3.0x) for medical accuracy
Legal/Finance: Moderate-high weights (1.2x-2.5x) for compliance
Journalism: Standard weights (1.0x-2.2x) for balanced reporting
Education: Slightly lenient (0.8x-1.8x) to encourage learning
Creative/Marketing: Very low weights (0.5x-1.2x) for flexibility
🔧 Technical Innovation:
Dynamic Weight Selection: Industry, content type, regulatory environment
Adaptive Learning: Feedback loops with industry experts
Regulatory Compliance: Integration with HIPAA, SOX, GDPR standards
Micro-Industry Specialization: Cardiology, Oncology, Corporate Law, etc.
💡 Key Insight:
The brilliance isn't just different weights—it's dynamic adaptation that considers:
1.Content type (factual vs. creative)
2.Industry context (healthcare vs. marketing)
3.Regulatory environment (strict vs. flexible)
4.Risk tolerance (zero-tolerance vs. acceptable risk)