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HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations
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HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations
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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.
Forward citations
Cited by 9 Pith papers
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HalluScore: Large Language Model Hallucination Question Answering Benchmark
HalluScore is a curated Arabic QA dataset with 827 questions, ground-truth evidence, and human annotations used to measure hallucination rates across 17 LLMs.
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Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Execution
A runtime governance framework for embodied agents achieves 96.2% interception of unauthorized actions and 91.4% recovery success in 1000 simulation trials by externalizing policy enforcement.
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HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering
HalluTruthQA provides 2,400 expert-annotated Arabic QA examples with character-level hallucination spans, explanations, and verification candidates, and shows no single LLM excels at all four evaluation tasks.
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HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering
HalluTruthQA contributes 2,400 expert-annotated Arabic QA examples with hallucination labels, error spans, human explanations, and candidate answers; evaluations show no open LLM leads across all four tasks.
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Graded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal Steering
Prompt-point activations carry a graded, steerable entity-familiarity signal that is robust to Polish/English stem changes and is stronger in Polish-adapted models than in base models.
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Does Bielik Know What It Doesn't Know? Activation Dispersion Separates Entity Familiarity from Factual Reliability Across Model Scale
Unsupervised MLP activation dispersion separates known from fabricated entities at AUROC 0.95–1.00 across Bielik scales, while factual reliability scales separately and refusals stay near zero.
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PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations
PRISM benchmark disentangles LLM hallucinations into knowledge missing, knowledge errors, reasoning errors, and instruction-following errors across three generation stages, revealing trade-offs when testing 24 models.
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Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Execution
An external runtime governance layer for embodied agents intercepts unauthorized actions at ~96% and recovers from runtime drift at ~91% under policy constraints in simulation, outperforming pre-execution-only baselin...
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Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Execution
A runtime governance framework for embodied agents intercepts 96.2% of unauthorized actions and achieves 91.4% recovery success in 1000 simulation trials while outperforming baselines.
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