# Hallucination Detection

This model is designed to detect hallucination phenomena in language model outputs, i.e., responses that are coherent but factually incorrect or out of context.

A claim verification model fine-tuned from Llama-3.2-3B-Instruct, specifically designed to detect hallucinations or unsupported statements in AI-generated text.

This model is specifically fine-tuned for SemEval 2025 Task3: Mu-SHROOM competition to identify hallucinated text segments in large language model outputs.

HHEM-2.1-Open is a hallucination detection model developed by Vectara, designed to evaluate the consistency between content generated by large language models and given evidence.