What Makes a Good Fiqh Retriever? Answer Retrieval for Arabic Islamic Jurisprudence

• URL: http://arxiv.org/abs/2608.20246v1
• Date: Thu, 20 Aug 2026 16:42:16 GMT
• Title: What Makes a Good Fiqh Retriever? Answer Retrieval for Arabic Islamic Jurisprudence
• Abstract summary: We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question.<n>Best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553.
• Score: 0.16752458252726457
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Retrieval-Augmented Generation is used for Islamic question answering, but most systems are evaluated end-to-end, making retrieval failures difficult to isolate from generation failures. We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question. We build a retrieval test collection for Arabic fiqh and use it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies. The best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553. Hybrid retrieval provides limited gains for strong models, whereas madhhab-aware filtering more than doubles MRR@5 on school-specific questions. We further present an error analysis showing that the main challenge is distinguishing answer-bearing passages from topically similar passages that do not contain the requested ruling.

Related papers

• Hybrid Neural Retrieval with Generative Query Refinement for Quranic Passage Retrieval [0.25489046505746704]
  Quranic Passage Retrieval (PR) could be a challenging task due to the linguistic complexity and the semantic gap between the Modern Standard Arabic (MSA) and the Classical Arabic (CA) of the Holy Quran.<n>This paper proposes a four-phase neural architecture designed to enhance retrieval accuracy and contextual understanding.
  arXiv  Detail & Related papers  (2026-06-11T19:06:57Z)
• Test-Time Strategies for More Efficient and Accurate Agentic RAG [58.44913384057518]
  Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions.<n>Such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information.<n>We investigate test-time modifications to the Search-R1 pipeline to mitigate these identified shortcomings.
  arXiv  Detail & Related papers  (2026-03-12T19:18:59Z)
• Inferential Question Answering [67.54465021408724]
  We introduce Inferential QA -- a new task that challenges models to infer answers from answer-supporting passages which provide only clues.<n>To study this problem, we construct QUIT (QUestions requiring Inference from Texts) dataset, comprising 7,401 questions and 2.4M passages.<n>We show that methods effective on traditional QA tasks struggle in inferential QA: retrievers underperform, rerankers offer limited gains, and fine-tuning provides inconsistent improvements.
  arXiv  Detail & Related papers  (2026-02-01T14:02:43Z)
• From RAG to Agentic RAG for Faithful Islamic Question Answering [12.67590523116037]
  We introduce ISLAMICFAITHQA, a 3,810-item bilingual (Arabic/English) generative benchmark with atomic single-gold answers.<n>We also develop an agentic Quran-grounding framework (agentic RAG) that uses structured tool calls for iterative evidence seeking and answer revision.
  arXiv  Detail & Related papers  (2026-01-12T13:28:28Z)
• Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction [0.4349640169711269]
  Quranic Question Answering presents unique challenges due to the linguistic complexity of Classical Arabic and the semantic richness of religious texts.<n>We propose a novel two-stage framework that addresses both passage retrieval and answer extraction.<n>Our approach achieves state-of-the-art results on the Quran QA 2023 Shared Task, with a MAP@10 of 0.3128 and MRR@10 of 0.5763 for retrieval, and a pAP@10 of 0.669 for extraction.
  arXiv  Detail & Related papers  (2025-08-09T12:37:19Z)
• ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision [23.80886911344813]
  Multi-hop question answering involves reasoning across multiple documents to answer complex questions.<n>Dense retrievers typically outperform sparse methods like BM25 by leveraging semantic embeddings.<n>ReSCORE is a novel method for training dense retrievers for MHQA without labeled documents.
  arXiv  Detail & Related papers  (2025-05-27T14:28:24Z)
• Cross-Language Approach for Quranic QA [1.0124625066746595]
  The Quranic QA system holds significant importance as it facilitates a deeper understanding of the Quran, a Holy text for over a billion people worldwide.<n>These systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic.<n>We adopt a cross-language approach by expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements.
  arXiv  Detail & Related papers  (2025-01-29T07:13:27Z)
• Hybrid and Collaborative Passage Reranking [144.83902343298112]
  We propose a Hybrid and Collaborative Passage Reranking (HybRank) method. It incorporates the lexical and semantic properties of sparse and dense retrievers for reranking. Built on off-the-shelf retriever features, HybRank is a plug-in reranker capable of enhancing arbitrary passage lists.
  arXiv  Detail & Related papers  (2023-05-16T09:38:52Z)
• Adversarial Retriever-Ranker for dense text retrieval [51.87158529880056]
  We present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. AR2 consistently and significantly outperforms existing dense retriever methods. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%)
  arXiv  Detail & Related papers  (2021-10-07T16:41:15Z)
• Phrase Retrieval Learns Passage Retrieval, Too [77.57208968326422]
  We study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents. We show that a dense phrase-retrieval system, without any retraining, already achieves better passage retrieval accuracy. We also show that phrase filtering and vector quantization can reduce the size of our index by 4-10x.
  arXiv  Detail & Related papers  (2021-09-16T17:42:45Z)
• Joint Passage Ranking for Diverse Multi-Answer Retrieval [56.43443577137929]
  We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms.
  arXiv  Detail & Related papers  (2021-04-17T04:48:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.