ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine

Large Language Models (LLMs) have demonstrated remarkable potential in diverse domains, yet their application in the legal sector, particularly in low-resource contexts, remains limited. This study addresses the challenges of adapting LLMs to the Palestinian legal domain, where political instability, fragmented legal frameworks, and limited AI resources hinder effective machine-learning applications. We present a fine-tuned model based on a quantized version of Llama-3.2-1B-Instruct, trained on a synthetic data set derived from Palestinian legal texts. Using smaller-scale models and strategically generated question-answer pairs, we achieve a cost-effective, locally sustainable solution that provides accurate and contextually relevant legal guidance. Our experiments demonstrate promising performance on various query types, ranging from yes/no questions and narrative explanations to complex legal differentiations, while highlighting areas for improvement, such as handling calculation-based inquiries and structured list formatting. This work provides a pathway for the deployment of AI-driven legal assistance tools tailored to the needs of resource-constrained environments.

Towards the Exploitation of LLM-based Chatbot for Providing Legal Support to Palestinian Cooperatives

With the ever-increasing utilization of natural language processing (NLP), we started to witness over the past few years a significant transformation in our interaction with legal texts. This technology has advanced the analysis and enhanced the understanding of complex legal terminology and contexts. The development of recent large language models (LLMs), particularly ChatGPT, has also introduced a revolutionary contribution to the way that legal texts can be processed and comprehended. In this paper, we present our work on a cooperative-legal question-answering LLM-based chatbot, where we developed a set of legal questions about Palestinian cooperatives, associated with their regulations and compared the auto-generated answers by the chatbot to their correspondences that are designed by a legal expert. To evaluate the proposed chatbot, we have used 50 queries generated by the legal expert and compared the answers produced by the chart to their relevance judgments. Finding demonstrated that an overall accuracy rate of 82% has been achieved when answering the queries, while exhibiting an F1 score equivalent to 79%.