LLMs Reshape Hadith Computational Science, But Gaps Remain

A new critical review published on arXiv examines the impact of large language models (LLMs) on hadith computational science. The paper highlights advancements in data resources, segmentation tasks, and LLM-assisted workflows for corpus enrichment and multilingual access. However, it also points out significant limitations, including narrow corpora, weak benchmark comparability, and sparse expert-grounded validation, arguing that progress is constrained by these factors. The review proposes a research agenda focused on knowledge integration, provenance, and expert supervision to strengthen the field and its utility for Islamic scholarship. AI

IMPACT Highlights the need for better knowledge integration and expert supervision in applying LLMs to specialized domains like Islamic scholarship.

RANK_REASON Academic paper published on arXiv discussing the application of LLMs to a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• fiqh
• hadith computational science
• Hugging Face
• large language models
• retrieval-grounded pipelines
• transformer models
• ulema

• paper
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