Hallucination Detection

Hallucination detection in large language models (LLMs) focuses on identifying instances where models generate plausible-sounding but factually incorrect information. Current research explores various approaches, including analyzing internal model representations (hidden states), leveraging unlabeled data, and employing ensemble methods or smaller, faster models for efficient detection. This is a critical area because accurate and reliable LLM outputs are essential for trustworthy applications across numerous domains, from healthcare and autonomous driving to information retrieval and code generation.