阿拉伯语语义搜索及其在检索增强生成（RAG）中作用的评估
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language
AI总结：
针对阿拉伯语语义搜索缺乏标准基准的挑战，本文提出了一个简单有效的基准，并在检索增强生成（RAG）框架内评估了语义搜索指标与数据集的有效性。
摘要：
机器学习和深度学习的最新进展带来了语义相似度的概念，该概念在多种应用中被证明极为有益，并在很大程度上取代了关键词搜索。然而，评估语义相似度并在各种文档中针对特定查询进行搜索仍然是一项复杂的任务。这种复杂性源于任务的多面性以及缺乏标准基准，而对于阿拉伯语而言，这些挑战被进一步放大。本文致力于为阿拉伯语语义搜索建立一个简单但有效的基准。此外，为了准确评估这些指标和数据集的有效性，我们在检索增强生成（RAG）框架内对语义搜索进行了评估。
英文摘要：
The latest advancements in machine learning and deep learning have brought forth the concept of semantic similarity, which has proven immensely beneficial in multiple applications and has largely replaced keyword search. However, evaluating semantic similarity and conducting searches for a specific query across various documents continue to be a complicated task. This complexity is due to the multifaceted nature of the task, the lack of standard benchmarks, whereas these challenges are further amplified for Arabic language. This paper endeavors to establish a straightforward yet potent benchmark for semantic search in Arabic. Moreover, to precisely evaluate the effectiveness of these metrics and the dataset, we conduct our assessment of semantic search within the framework of retrieval augmented generation (RAG).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
