Explainable Hallucination through Natural Language Inference Mapping

Large language models ({LLMs}) often generate hallucinated content, making it crucial to identify and quantify inconsistencies in their outputs. We introduce {HaluMap}, a post-hoc framework that detects hallucinations by mapping entailment and contradiction relations between source inputs and generated outputs using a natural language inference ({NLI}) model. To improve reliability, we propose a calibration step leveraging intra-text relations to refine predictions. {HaluMap} outperforms state-of-the-art {NLI}-based methods by five percentage points compared to other training-free approaches, while providing clear, interpretable explanations. As a training-free and model-agnostic approach, {HaluMap} offers a practical solution for verifying {LLM} outputs across diverse {NLP} tasks. The resources of this paper are available at https://github.com/caisa-lab/acl25-halumap.