DSC2025 -- ViHallu Challenge: Detecting Hallucination in Vietnamese LLMs

• URL: http://arxiv.org/abs/2601.04711v1
• Date: Thu, 08 Jan 2026 08:27:47 GMT
• Title: DSC2025 -- ViHallu Challenge: Detecting Hallucination in Vietnamese LLMs
• Authors: Anh Thi-Hoang Nguyen, Khanh Quoc Tran, Tin Van Huynh, Phuoc Tan-Hoang Nguyen, Cam Tan Nguyen, Kiet Van Nguyen,
• Abstract summary: This paper introduces the DSC2025 ViHallu Challenge, the first large-scale shared task for detecting hallucinations in Vietnamese language models.<n>We present the ViHallu dataset, comprising 10,000 annotated triplets of (context, prompt, response) samples.<n>A total of 111 teams participated, with the best-performing system achieving a macro-F1 score of 84.80%, compared to a baseline encoder-only score of 32.83%.
• Score: 5.740643252319679
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The reliability of large language models (LLMs) in production environments remains significantly constrained by their propensity to generate hallucinations--fluent, plausible-sounding outputs that contradict or fabricate information. While hallucination detection has recently emerged as a priority in English-centric benchmarks, low-to-medium resource languages such as Vietnamese remain inadequately covered by standardized evaluation frameworks. This paper introduces the DSC2025 ViHallu Challenge, the first large-scale shared task for detecting hallucinations in Vietnamese LLMs. We present the ViHallu dataset, comprising 10,000 annotated triplets of (context, prompt, response) samples systematically partitioned into three hallucination categories: no hallucination, intrinsic, and extrinsic hallucinations. The dataset incorporates three prompt types--factual, noisy, and adversarial--to stress-test model robustness. A total of 111 teams participated, with the best-performing system achieving a macro-F1 score of 84.80\%, compared to a baseline encoder-only score of 32.83\%, demonstrating that instruction-tuned LLMs with structured prompting and ensemble strategies substantially outperform generic architectures. However, the gap to perfect performance indicates that hallucination detection remains a challenging problem, particularly for intrinsic (contradiction-based) hallucinations. This work establishes a rigorous benchmark and explores a diverse range of detection methodologies, providing a foundation for future research into the trustworthiness and reliability of Vietnamese language AI systems.

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