用少样本提示让大模型精准提取古兰经答案，减少幻觉。
Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs
- 设计阿拉伯语提示框架，结合指令微调大模型进行片段抽取。
- 最佳配置在pAP10上达0.637，优于传统微调模型。
- 适合低资源、语义复杂的宗教文本问答任务研究者。
本文提出两种针对古兰经的抽取式问答方法。针对文本语言复杂、术语独特、意义深远等挑战，第二项方法采用少样本提示技术，结合如Gemini和DeepSeek等指令微调的大语言模型。开发了专用阿拉伯语提示框架以实现跨度抽取，并构建强后处理系统，包含子词对齐、重叠抑制和语义过滤，有效提升精确率并减少幻觉。评估显示，使用阿拉伯语指令的大模型性能优于传统微调模型。最优配置在pAP10指标上达到0.637。结果表明，基于提示的指令微调在低资源、语义丰富的问答任务中具有显著有效性。
原文摘要 · Abstract (English)
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.
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