Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval

핵심 개념

Using large language models for automated relevance judgments in legal case retrieval.

초록

The article discusses the challenges in collecting relevant judgments for legal case retrieval and proposes a novel workflow using large language models. It breaks down the annotation process into stages, mimicking human annotators' process. The method shows promising results in obtaining reliable relevance judgments and augmenting legal case retrieval models.

Introduction: Legal case retrieval importance.
Challenges: Keyword-based retrieval systems inefficiency.
Proposed Workflow: Few-shot workflow for relevance judgment.
Data Annotation: Comparison of LLMs and human experts.
Key Challenges: Expertise-intensive, lengthy-text, nuance-sensitive.
Methodology: Preliminary factual analysis, adaptive demo-matching, fact extraction, few-shot annotation.
Application: Data augmentation for synthetic dataset creation.
Experiment: Evaluation of annotations and data augmentation.
Results: Reliability and validity of annotations, impact of different components on annotation quality.

통계

Accurately judging the relevance between two legal cases requires a considerable effort to read the lengthy text and a high level of domain expertise.
Large-scale Language Models (LLMs) are designed to understand and generate human-like text with little to no fine-tuning required for specific tasks.
The LeCaRD dataset comprises more than 43,000 candidate cases and 107 query cases.

인용구

"The proposed workflow breaks down the annotation process into a series of stages, imitating the process employed by human annotators."
"Empirical experiments demonstrate that our approach can achieve high consistency with expert annotations."

핵심 통찰 요약

Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval

by Shengjie Ma,... 게시일 arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18405.pdf

더 깊은 질문

How can the proposed workflow be adapted for legal systems in other countries?

The proposed workflow for automated relevance judgments in legal case retrieval can be adapted for legal systems in other countries by customizing the annotations and demonstrations to align with the specific legal frameworks and nuances of those countries. This adaptation would involve collaborating with legal experts from the respective countries to create a set of expert demonstrations that reflect the unique characteristics of their legal systems. Additionally, the language model used in the workflow can be fine-tuned on legal texts from the target country to enhance its understanding of the local legal language and context. By tailoring the workflow to the specific requirements of different legal systems, the automated relevance judgments can be effectively applied in a global context.

What are the potential biases in automated relevance judgments using large language models?

Automated relevance judgments using large language models may be susceptible to several biases. One potential bias is the model's reliance on the training data, which can introduce biases present in the data itself. If the training data is not diverse or representative of all perspectives, the model may learn and perpetuate those biases in its judgments. Another bias could arise from the design of the prompts and demonstrations used to guide the model in making relevance judgments. If the prompts are not carefully crafted to be neutral and comprehensive, they may inadvertently introduce biases into the model's decision-making process. Additionally, the inherent limitations of large language models, such as their inability to fully understand context or nuances, can lead to biased judgments based on surface-level information rather than a deep understanding of the legal content.

How can the concept of automated relevance judgments be applied to other fields beyond legal case retrieval?

The concept of automated relevance judgments can be applied to various fields beyond legal case retrieval by adapting the workflow to suit the specific requirements of those domains. For example, in healthcare, large language models can be used to automatically assess the relevance of medical records or research articles to a particular diagnosis or treatment plan. In academic research, automated relevance judgments can help researchers identify relevant literature for literature reviews or citation analysis. In e-commerce, the concept can be applied to improve product recommendations by automatically determining the relevance of products to a customer's preferences. By customizing the workflow and training the language model on domain-specific data, automated relevance judgments can streamline decision-making processes and enhance efficiency in a wide range of industries.