Summary of Alkafi-llama3: Fine-tuning Llms For Precise Legal Understanding in Palestine, by Rabee Qasem et al.
ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine
by Rabee Qasem, Mohannad Hendi, Banan Tantour
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a fine-tuned version of a large language model (LLM) adapted to the Palestinian legal domain, where limited AI resources and fragmented legal frameworks hinder its application. The authors train the model on synthetic data derived from Palestinian legal texts and achieve promising performance on various query types, including yes/no questions, narrative explanations, and complex legal differentiations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study shows how a cost-effective, locally sustainable solution can be achieved by using smaller-scale models and strategically generated question-answer pairs. The authors highlight areas for improvement, such as handling calculation-based inquiries and structured list formatting. |
Keywords
» Artificial intelligence » Large language model » Synthetic data
