Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models

TL;DR

This paper presents a novel LLM-based approach with specialized prompts to improve numerical estimation accuracy in legal AI applications, addressing efficiency and precision challenges in the legal domain.

Contribution

It introduces a new methodology combining LLMs and tailored prompts for precise legal numerical reasoning, supported by a curated benchmark dataset.

Findings

LLMs can generate accurate legal numerical estimates

The proposed method improves legal workflow efficiency

Benchmark results demonstrate high precision in legal AI tasks

Abstract

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.