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).

翻译：机器学习和深度学习的最新进展催生了语义相似性这一概念，该概念已在多项应用中展现出巨大价值，并在很大程度上取代了关键词搜索。然而，针对特定查询在多个文档间评估语义相似性并执行搜索仍是一项复杂任务。这种复杂性源于任务的多面性及标准化基准的缺失，而这些问题在阿拉伯语中尤为突出。本文致力于为阿拉伯语语义搜索建立一个简单而有效的基准。此外，为精确评估这些度量指标及数据集的效能，我们在检索增强生成（RAG）框架下对语义搜索进行了评估。