Evaluating Arabic Language Embedding Models for Semantic Retrieval in Fatwas
Bibliographic record
Abstract
The application of artificial intelligence to domain-specific textual analysis has opened new possibilities in natural language processing, particularly in the context of fatwas-authoritative Islamic legal opinions. This study presents a comparative evaluation of three language embedding models-Sentence Transformers, AraBERTv2, and MARBERT-within a Retrieval-Augmented Generation (RAG) framework powered by the Gemini model. A curated corpus of 285 texts, comprising fatwas, Qur’anic exegesis, and related jurisprudential writings, serves as the basis for evaluating semantic comprehension, retrieval accuracy, and generative performance. The findings demonstrate the superior performance of Arabic-specific models, with MARBERT and AraBERTv2 notably outperforming the multilingual Sentence Transformers in capturing the nuanced language and legal reasoning typical of fatwas. These results highlight the significance of culturally and linguistically specialized models in processing religious legal discourse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".