Abstract

A conversational system is a natural language processing task that has recently attracted increasing attention with the advancements in Large Language Models (LLMs) and Language Models for Dialogue Applications (LaMDA). However, Conversational Artificial Intelligence (AI) research has mainly been carried out in English. Despite the growing popularity of Arabic as one of the most widely used languages on the Internet, only a few studies have concentrated on Arabic conversational dialogue systems thus far. In this study, we conduct a comprehensive qualitative analysis of the key research works in this domain, examining the limitations and strengths of existing approaches. We start with chatbot history and classification. Then, we examine approaches that leverage Arabic chatbots Rule-based/Retrieval-based and Deep learning-based. In particular, we survey the evolution of Generative Conversational AI with the evolution of deep-learning techniques. Next, we look at the different metrics used to assess conversational systems. Finally, we outline language Challenges for building Generative Arabic Conversational AI.

Key words: Chatbot, LLMs, Arabic conversational AI Challenges, Generative Artificial Intelligence, Arabic question answering systems, Taxonomy, Performance Evaluation.