用集成模型+指令微调，提升古阿拉伯语《古兰经》问答准确率
Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction
- 分两阶段：先集成多个微调模型检索相关段落，再用指令微调大模型提取答案
- 检索阶段MAP@10达0.3128，排序效果优于此前方法；答案提取pAP@10为0.669
- 适合低资源宗教文本问答研究者，尤其关注古阿拉伯语自然语言处理
《古兰经》问答因古典阿拉伯语的语言复杂性和宗教文本的语义丰富性而面临独特挑战。本文提出一种新颖的两阶段框架，同时应对段落检索与答案抽取问题。在段落检索阶段，通过集成多个微调的阿拉伯语语言模型，实现更优的排序性能；在答案抽取阶段，采用指令微调的大语言模型并结合少样本提示，克服小数据集微调的局限。该方法在Quran QA 2023共享任务中取得当前最佳结果：检索阶段的MAP@10为0.3128，MRR@10为0.5763；答案抽取阶段的pAP@10为0.669，显著优于以往方法。结果表明，结合模型集成与指令微调大模型能有效应对专精领域低资源问答挑战。
原文摘要 · Abstract (English)
Quranic Question Answering presents unique challenges due to the linguistic complexity of Classical Arabic and the semantic richness of religious texts. In this paper, we propose a novel two-stage framework that addresses both passage retrieval and answer extraction. For passage retrieval, we ensemble fine-tuned Arabic language models to achieve superior ranking performance. For answer extraction, we employ instruction-tuned large language models with few-shot prompting to overcome the limitations of fine-tuning on small datasets. 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, substantially outperforming previous methods. These results demonstrate that combining model ensembling and instruction-tuned language models effectively addresses the challenges of low-resource question answering in specialized domains.
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