Основные понятия
Semantic search in Arabic language is crucial for enhancing the performance of RAG systems, requiring advanced encoders and evaluation metrics.
Аннотация
The content discusses the importance of semantic search in Arabic language processing, focusing on its role in improving the performance of Retrieved-Augmented-Generation (RAG) systems. The paper establishes a benchmark for semantic search in Arabic and evaluates its effectiveness within the RAG framework. It covers the evolution of semantic search, the challenges faced in Arabic language processing, the methodology for evaluation, the dataset generation process, evaluation metrics used, and the assessment of different encoders. The study also delves into the correlation between semantic search accuracy and RAG performance, highlighting the significance of incorporating semantic search into RAG systems for generating high-quality content. The results showcase the impact of different encoders on semantic search and RAG accuracy, emphasizing the need for further research to optimize NLP applications for Arabic-speaking users.
Статистика
The evaluation dataset comprises 2030 customer support call summaries and 406 search queries.
Encoder #3 (Paraphrase Multilingual Mpnet) performed best for Arabic semantic search.
Цитаты
"Semantic search interprets the meaning and relationships between words, aiming to mimic human understanding."
"RAG represents an innovative approach at the crossroads of information retrieval and natural language generation."
"Arabic RAG has not yet emerged as a focal point of scholarly inquiry to the degree that perhaps it warrants."