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Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs

Domaine:

natural language processing

Type de record:

paper
Créateur:
BasOshHamMohammed Ammar
Hôte:avatar
This paper presents two effective approaches for Extractive Question Answering (QA) on the Quran. It addresses challenges related to complex language, unique terminology, and deep meaning in the text. The second uses few-shot prompting with instruction-tuned large language models such as Gemini and DeepSeek. A specialized Arabic prompt framework is developed for span extraction. A strong post-processing system integrates subword alignment, overlap suppression, and semantic filtering. This improves precision and reduces hallucinations. Evaluations show that large language models with Arabic instructions outperform traditional fine-tuned models. The best configuration achieves a pAP10 score of 0.637. The results confirm that prompt-based instruction tuning is effective for low-resource, semantically rich QA tasks. 6 pages , 2 figures , Accepted in IMSA 2025,Egypt , imsa.msa.edu.eg

Visit

arxiv.org

Tasks

question answering

Tags

Computation and LanguageInformation Retrieval

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