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        <title>Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages</title>
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            <namePart type="given">Vera</namePart>
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    <abstract>Hallucinations generated by Large Language Models (LLMs) pose significant challenges for their application to low-resource languages. We present Multi-Hall-SA, a cross-lingual benchmark for hallucination detection spanning English and four low-resource South African languages: isiZulu, isiXhosa, Sepedi, and Sesotho. Derived from government texts, this benchmark categorizes hallucinations into four types aligned with established taxonomies of factual errors: temporal shifts, entity errors, numerical inaccuracies, and location mistakes. Human validation confirms the quality and cross-lingual alignment of our synthetically generated hallucinations. Our cross-lingual alignment methodology enables direct performance comparison between high-resource and low-resource languages, revealing notable gaps in detection capabilities. Evaluation across four state-of-the-art models shows they detect up to 23.6% fewer hallucinations in South African languages compared to English. Knowledge augmentation reduces this disparity, decreasing cross-lingual performance gaps by 59.4% on average. Beyond introducing a validated resource for low-resource languages, Multi-Hall-SA provides a framework for evaluating and improving factual reliability across linguistic boundaries, advancing more inclusive and equitable AI development.</abstract>
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%0 Conference Proceedings
%T Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages
%A Ralethe, Sello
%A Buys, Jan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F ralethe-buys-2026-multi
%X Hallucinations generated by Large Language Models (LLMs) pose significant challenges for their application to low-resource languages. We present Multi-Hall-SA, a cross-lingual benchmark for hallucination detection spanning English and four low-resource South African languages: isiZulu, isiXhosa, Sepedi, and Sesotho. Derived from government texts, this benchmark categorizes hallucinations into four types aligned with established taxonomies of factual errors: temporal shifts, entity errors, numerical inaccuracies, and location mistakes. Human validation confirms the quality and cross-lingual alignment of our synthetically generated hallucinations. Our cross-lingual alignment methodology enables direct performance comparison between high-resource and low-resource languages, revealing notable gaps in detection capabilities. Evaluation across four state-of-the-art models shows they detect up to 23.6% fewer hallucinations in South African languages compared to English. Knowledge augmentation reduces this disparity, decreasing cross-lingual performance gaps by 59.4% on average. Beyond introducing a validated resource for low-resource languages, Multi-Hall-SA provides a framework for evaluating and improving factual reliability across linguistic boundaries, advancing more inclusive and equitable AI development.
%U https://aclanthology.org/2026.findings-eacl.330/
%P 6282-6296

Markdown (Informal)

[Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages](https://aclanthology.org/2026.findings-eacl.330/) (Ralethe & Buys, Findings 2026)

• Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages (Ralethe & Buys, Findings 2026)

ACL

• Sello Ralethe and Jan Buys. 2026. Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages. In Findings of the Association for Computational Linguistics: EACL 2026, pages 6282–6296, Rabat, Morocco. Association for Computational Linguistics.