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arXiv · 2503.07833

HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations

Samir Abdaljalil·Hasan Kurban·Erchin Serpedin
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Abstract

Large Language Models (LLMs) are increasingly used in various contexts, yet remain prone to generating non-factual content, commonly referred to as "hallucinations". The literature categorizes hallucinations into several types, including entity-level, relation-level, and sentence-level hallucinations. However, existing hallucination datasets often fail to capture fine-grained hallucinations in multilingual settings. In this work, we introduce HalluVerse25, a multilingual LLM hallucination dataset that categorizes fine-grained hallucinations in English, Arabic, and Turkish. Our dataset construction pipeline uses an LLM to inject hallucinations into factual biographical sentences, followed by a rigorous human annotation process to ensure data quality. We evaluate several LLMs on HalluVerse25, providing valuable insights into how proprietary models perform in detecting LLM-generated hallucinations across different contexts.

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Samir Abdaljalil, Hasan Kurban, Erchin Serpedin. 2025-03-10. HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations. https://arxiv.org/abs/2503.07833

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