Natural Sciences

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https://hdl.handle.net/20.500.11888/18293

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Browsing Natural Sciences by Author "Al-Huwari, Samer"

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    NEBRAS: A RAG-BASED QUESTION ANSWERING SYSTEM FOR ISLAMIC AND LEGAL GUIDANCE
    (An-Najah National University, 2025-02-27) Al-Huwari, Samer
    Question answering (QA) systems are essential tools in natural language processing (NLP), designed to interpret user queries and generate relevant answers. These systems have evolved over time from rule-based models to advanced machine-learning-based approaches. The emergence of the transformers architecture and Large Language Models (LLMs) have set the stage for modern QA systems. LLMs have transformed QA by leveraging vast datasets to generate human-like responses across various domains and their ability to understand complex linguistic patterns. However, LLMs often generates plausible but incorrect answers particularly in specialized domains like law and religion where accuracy is critical. This phenomenon is known as “hallucination”. The risk of “hallucination” is increased when dealing with a complex language like Arabic. Arabic language, with its rich morphology, diverse dialects, and its dependency on diacritics, present significant challenges for LLMs primarily trained on Western languages. Fine-tuning LLMs for domain-specific tasks is time-intensive, and computationally-expensive, given their massive parameters size, demanding innovative approaches to mitigate the LLMs hallucination issue without extensive re-training. This thesis introduces Nebras, a generic multi-domain QA system leveraging a Retrieval-Augmented Generation (RAG) framework, LLM agents, and a hybrid retrieval approach. Nebras’s knowledge base can be dynamically extended by following simple guidelines and using its built-in mapping component, enabling it to adapt to any textual dataset. By employing an Agentic RAG pipeline, Nebras optimizes each processing stage using specialized agents. Furthermore, it utilizes pre-trained LLMs without fine-tuning, enhancing scalability and reducing computational costs. Experimentation results demonstrated Nebras’s performance in Arabic domain-specific QA. In the Islamic fatwa domain, it achieved a BERTScore-F1 of 70.94%, a METEOR of 13.49%, with 9 accepted fatwas compared to only 7 accepted from GPT-4o. In the university help-desk domain, Nebras achieved a BERTScore-F1 of 75.80%, METEOR of 40.20%, and BLEU of 9%, significantly outperforming the BLEU score of 2.3% from GPT-4o's. These results highlight Nebras's ability to enhance factual accuracy, confirming its potential as a scalable Arabic QA solution.