Hallucination Detection
Techniques for automatically identifying when AI models generate factually incorrect or unsupported information.
Overview
Hallucination detection aims to automatically identify when language models generate statements that are factually incorrect, unsupported by provided context, or internally inconsistent. This is critical for deploying LLMs in high-stakes applications where incorrect information could cause harm.
Approaches
Techniques include: Self-consistency: Sampling multiple outputs and checking for agreement. External verification: Fact-checking against knowledge bases or search. Entailment checking: Verifying that outputs are supported by the input context. Confidence calibration: Using model uncertainty signals. Specialized models: Training classifiers specifically to detect hallucinated content.