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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2412.14771 |
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| _version_ | 1866910752782155776 |
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| author | Qasem, Rabee Hendi, Mohannad Tantour, Banan |
| author_facet | Qasem, Rabee Hendi, Mohannad Tantour, Banan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine Qasem, Rabee Hendi, Mohannad Tantour, Banan Computation and Language Artificial Intelligence Machine Learning 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. |
| title | ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.14771 |