Leveraging Large Language Models to Enhance Arabic Prompting and Response Generation in ChatGPT

Abstract

This paper presents a new system of initiation of ChatGPT's Arabic prompts and responses Enhancement and Evaluation Systems, to improve the quality of interaction with large language models (LLMs) through automated role-based prompt. This new system, which is designed specifically for novice end-users, automatically detects the intended field and behavioral role of Arabic prompts. This is done by integrating with a multilingual Natural Language Inference model (mDeBERTa-v3) for classification, and advanced techniques of prompt enhancement using GPT-4o. Enrichment of coordinated yet detailed contextualized instructions enhances prompts. Original and engineered prompts and responses are evaluated using a new evaluation method based on GPT-Eval, emphasizing completeness, clarity, and relevance. A rich set of 1,990 Arabic claims showed interesting results for tracking assessments to improve performance. It infers that the margin grows from 70.0% to 99.0% between the unmodified and enhanced prompts and responses. Overall, this approach significantly beats any of the classical methods and offers completely new pictures of how automated Arabic prompt might be drawn toward maximizing LLM performance for Arabic NLP applications.

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