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        <title>A Logic-Based Approach to Hallucinations in Data-to-Text NLG: Experiments with Human and LLM Annotators</title>
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    <abstract>Hallucinations are a persistent challenge in natural language generation, including data-to-text. van Deemter (2024) introduced a framework based on the relation of logical consequence (”follows from”), which divides all data-to-text hallucinations into seven disjoint categories. We examine whether human annotators and large language models are able to apply the framework, in two data-to-text domains. Results suggest that the framework is applicable, although there are significant domain-dependent variations, as well as discrepancies between human and model judgments. We also uncover several issues that should inform future work on hallucination.</abstract>
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%0 Conference Proceedings
%T A Logic-Based Approach to Hallucinations in Data-to-Text NLG: Experiments with Human and LLM Annotators
%A Calò, Eduardo
%A Mahamood, Saad
%A Gatt, Albert
%A Van Deemter, Kees
%Y Mohammad, Saif M.
%Y Ousidhoum, Nedjma
%S Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-413-2
%F calo-etal-2026-logic
%X Hallucinations are a persistent challenge in natural language generation, including data-to-text. van Deemter (2024) introduced a framework based on the relation of logical consequence (”follows from”), which divides all data-to-text hallucinations into seven disjoint categories. We examine whether human annotators and large language models are able to apply the framework, in two data-to-text domains. Results suggest that the framework is applicable, although there are significant domain-dependent variations, as well as discrepancies between human and model judgments. We also uncover several issues that should inform future work on hallucination.
%U https://aclanthology.org/2026.starsem-conference.3/
%P 28-62

Markdown (Informal)

[A Logic-Based Approach to Hallucinations in Data-to-Text NLG: Experiments with Human and LLM Annotators](https://aclanthology.org/2026.starsem-conference.3/) (Calò et al., *SEM 2026)

• A Logic-Based Approach to Hallucinations in Data-to-Text NLG: Experiments with Human and LLM Annotators (Calò et al., *SEM 2026)

ACL

• Eduardo Calò, Saad Mahamood, Albert Gatt, and Kees Van Deemter. 2026. A Logic-Based Approach to Hallucinations in Data-to-Text NLG: Experiments with Human and LLM Annotators. In Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), pages 28–62, San Diego, California, United States. Association for Computational Linguistics.