Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Jia-Hong Huang, Chao-Chun Yang, Yixian Shen, Alessio M. Pacces, Evangelos Kanoulas

TL;DR

The paper tackles the challenge of delivering precise numerical estimates in diverse legal scenarios under expert scarcity by combining Large Language Models with in-context learning and specially designed prompts. It introduces a precision-oriented LegalAI dataset focused on numerical estimation (house-value cases) and demonstrates, through experiments with GPT-3.5, GPT-4, Claude, and Bard Gemini, that larger LLMs with crafted prompts achieve substantial accuracy gains, notably with GPT-4 achieving a $MAPE$ of $15.71\%$ on the task. Key contributions include a novel prompt-design framework for numerical estimation in law, an in-context learning strategy to bypass fine-tuning, and a real-world dataset (58 samples; 45 train, 13 test) for validating precision-oriented LegalAI tasks. The results indicate that LLM-driven precision methods can enhance legal workflows by providing rapid, objective estimates (e.g., compensation and sentencing proxies) while highlighting the need for interpretability and bias-auditing to ensure ethical deployment and broad accessibility.

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.

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

TL;DR

The paper tackles the challenge of delivering precise numerical estimates in diverse legal scenarios under expert scarcity by combining Large Language Models with in-context learning and specially designed prompts. It introduces a precision-oriented LegalAI dataset focused on numerical estimation (house-value cases) and demonstrates, through experiments with GPT-3.5, GPT-4, Claude, and Bard Gemini, that larger LLMs with crafted prompts achieve substantial accuracy gains, notably with GPT-4 achieving a of on the task. Key contributions include a novel prompt-design framework for numerical estimation in law, an in-context learning strategy to bypass fine-tuning, and a real-world dataset (58 samples; 45 train, 13 test) for validating precision-oriented LegalAI tasks. The results indicate that LLM-driven precision methods can enhance legal workflows by providing rapid, objective estimates (e.g., compensation and sentencing proxies) while highlighting the need for interpretability and bias-auditing to ensure ethical deployment and broad accessibility.

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.
Paper Structure (14 sections, 1 equation, 4 figures, 5 tables)

This paper contains 14 sections, 1 equation, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Conventional AI in Legal Contexts
  4. LegalAI Datasets
  5. Large Language Models
  6. Methodology
  7. In-context Learning
  8. Prompts Design
  9. Proposed Precision-Oriented LegalAI Dataset
  10. Experiments and Analysis
  11. Experimental Settings
  12. Evaluation and Analysis
  13. Discussion
  14. Conclusion and Future Work

Figures (4)

  • Figure 1: Flowchart of practical legal proceedings. When clients or litigants bring their lawsuits to a law firm, they often pose questions pertaining to their primary concerns, such as financial compensation or imprisonment duration. However, providing prompt answers to such queries is challenging, as lawyers may need considerable time to review extensive legal documentation and precedents to offer well-informed responses. Furthermore, if the lawsuit involves numerical estimations related to other fields, such as estimating asset values, additional experts from those fields, such as asset valuation experts, are often required, further prolonging the problem-solving process.
  • Figure 2: Design of an example prompt for compensation amount estimation, as depicted in (a). Additionally, the corresponding response generated by GPT-4 is included to ensure the completeness of this question-answering conversion, referenced in (b). The ground truth answers for each judgment in the example are $200,000$ NTD, $150,000$ NTD, $250,000$ NTD, $260,000$ NTD, and $200,000$ NTD, respectively.
  • Figure 3: Creation of an example prompt for imprisonment duration estimation, illustrated in (a). Furthermore, the corresponding response generated by GPT-4 is provided to ensure the comprehensiveness of this question-answering conversion, as indicated in (b). The ground truth answers for each judgment in the example are as follows: 17.2 months, 96.76 months, 3.28 months, 57.64 months, 11.44 months, 4.08 months, 28.28 months, 209.6 months, 8 months, and 22 months, respectively.
  • Figure 4: Randomly selected examples from the proposed dataset. Each row represents a data sample with nine different properties.