HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification
Score
80首次提供阿拉伯语细粒度幻觉标注语料，包含字符级错误跨度和分层类型，信息增量显著。
构建流程严谨，包含专家标注、独立验证和仲裁机制，数据统计与标注指南完整。
可直接用于幻觉检测、错误定位和解释生成等任务，并作为官方共享任务数据集，实用性强。
面向2026年共享任务，紧跟当前大模型幻觉研究热点，时效性较高。
来源于arXiv预印本，作者提供详细方法论与数据统计，信源可靠且透明。
Base 88.5 × T2 Source weight ×0.9 = 80
Curation line 70Selected
Why selected
The paper offers a large-scale, fine-grained, expert-verified Arabic hallucination corpus with complete methodology, standing out for its practicality and scarcity.
AI commentary
「The most notable aspect is the move from coarse binary hallucinations to character-level span annotations for a resource-poor language like Arabic. For researchers in multilingual NLP or Arabic LLM reliability, this is a ready-to-use resource. However, as a preprint, its quality and impact will ultimately be validated by the shared task outcomes and community adoption.」
Quick overview
Large language models can generate fluent Arabic answers while introducing factual errors that are difficult to identify and verify. Existing Arabic hallucination resources often assign a binary label to an entire response, providing limited information about the exact erroneous content, the reason for the error, or the correct factual answer. This paper presents HalluTruthQA-4K, an expanded version of the HalluTruthQA resource containing 4,000 expert-curated Arabic question-answering instances across four knowledge-intensive domains: Islamic knowledge, history, science, and geography. Each instance pairs an Arabic question with a model-generated response, a verified reference answer, and five plausible distractors. Hallucinated responses are additionally annotated with character-level erroneous spans, human-written explanations, and hierarchical hallucination types. The corpus contains 1,643 hallucinated and 2,357 non-hallucinated responses, with 1,843 annotated erroneous spans. The paper details resource construction, annotation guidelines, taxonomy, data format, inter-annotator agreement, and corpus statistics, providing a reusable resource for hallucination detection, span-level error localization, explanation generation, factual verification, and broader evaluation of factual reliability in Arabic language models.
- HalluTruthQA-4K contains 4,000 expert-curated Arabic QA instances across Islamic knowledge, history, science, and geography.
- Unlike existing binary-label resources, it provides character-level erroneous spans, human-written explanations, and hierarchical hallucination types.
- The corpus includes 1,643 hallucinated and 2,357 non-hallucinated responses, with 1,843 annotated erroneous spans.
- Serving as the official dataset for Track 2 of the HalluScoring 2026 shared task, it supports hallucination detection, error localization, explanation generation, and fact verification.
Full text
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