Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language
Year: 2024
Type: preprint
Abstract: 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).
Source: arXiv (Cornell University)
Authors: Mahboub, Ali, Za'ter, Muhy Eddin, Al-Rfooh, Bashar, Estaitia, Yazan, Jaljuli, Adnan, Hakouz, Asma
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4393300183