The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with challenges in delivering timely and accurate information to clients, particularly concerning critical aspects like potential imprisonment duration or financial repercussions. Compounded by the scarcity of legal experts, there's an urgent need to enhance the efficiency of traditional legal workflows. Recent advances in deep learning, especially Large Language Models (LLMs), offer promising solutions to this challenge. Leveraging LLMs' mathematical reasoning capabilities, we propose a novel approach integrating LLM-based methodologies with specially designed prompts to address precision requirements in legal Artificial Intelligence (LegalAI) applications. The proposed work seeks to bridge the gap between traditional legal practices and modern technological advancements, paving the way for a more accessible, efficient, and equitable legal system. To validate this method, we introduce a curated dataset tailored to precision-oriented LegalAI tasks, serving as a benchmark for evaluating LLM-based approaches. Extensive experimentation confirms the efficacy of our methodology in generating accurate numerical estimates within the legal domain, emphasizing the role of LLMs in streamlining legal processes and meeting the evolving demands of LegalAI.

本研究旨在解决法律领域中律师在提供及时和准确客户信息时面临的挑战，尤其是在潜在监禁时长和财务影响方面。我们提出了一种新颖的方法，将基于大型语言模型的技术与特别设计的提示相结合，以提高法律人工智能应用中的精确度。实验结果表明，该方法有效地生成了法律领域的准确数值估计，为提高法律流程的效率与公平性提供了可能的解决方案。

通过大型语言模型优化法律领域的数值估计和操作效率