Category:
Category:
Fact-Checking Models
Category:
Evaluation & Quality
Definition
Models designed to evaluate factual correctness of LLM outputs.
Explanation
Fact-checking models evaluate LLM outputs by comparing them against trusted sources, retrieval systems, structured databases, or domain-specific knowledge. They can classify statements as true/false, provide supporting evidence, or rewrite incorrect claims. Enterprises use them to ensure safety and compliance.
Technical Architecture
LLM Output → Fact-Checker → Evidence Retrieval → Verdict → Output
Core Component
Retriever, verifier model, evidence collector, contradiction detector
Use Cases
Analytics, research tools, compliance, regulated industry AI
Pitfalls
Incorrect verdicts when retrieval fails; hallucinated evidence.
LLM Keywords
AI Fact Checking, LLM Evidence Verification
Related Concepts
Related Frameworks
• Verification Layers
• RAG
• Hallucinations
• Fact Verification Pipeline