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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Explainable Hallucination through Natural Language Inference Mapping
%A Chen, Wei-Fan
%A Zhao, Zhixue
%A Karimi, Akbar
%A Flek, Lucie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-explainable
%X 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.
%R 10.18653/v1/2025.findings-acl.96
%U https://aclanthology.org/2025.findings-acl.96/
%U https://doi.org/10.18653/v1/2025.findings-acl.96
%P 1888-1896

Markdown (Informal)

[Explainable Hallucination through Natural Language Inference Mapping](https://aclanthology.org/2025.findings-acl.96/) (Chen et al., Findings 2025)

• Explainable Hallucination through Natural Language Inference Mapping (Chen et al., Findings 2025)

ACL

• Wei-Fan Chen, Zhixue Zhao, Akbar Karimi, and Lucie Flek. 2025. Explainable Hallucination through Natural Language Inference Mapping. In Findings of the Association for Computational Linguistics: ACL 2025, pages 1888–1896, Vienna, Austria. Association for Computational Linguistics.