In “Comparative Analysis of the Risk of Hadith Errors in Question-Answering Systems Based on Large Language Models” (International Journal of Research and Scientific Innovation), Hakkun Elmunsyah (Universitas Negeri Malang) conducts a “comparative analysis of the risk of errors generated by Large Language Model-based Question-Answering systems in answering hadith-related questions. Four LLM models, namely GPT, Gemini, Claude, and LLaMA, were evaluated using a controlled set of designed prompts. The model outputs were analyzed and classified into four error categories: fabricated hadith, incorrect attribution, hybrid hadith, and distortion of meaning. The frequency of each error type was then analyzed comparatively to identify error patterns and risk levels for each model. The results show significant differences in the number and types of errors among LLM models, reflecting different risk profiles in the implementation of hadith QA systems.”
