Automating IRAC Analysis in Malaysian Contract Law using a Semi-Structured Knowledge Base
This work addresses the need for high-quality data in legal AI for Malaysian contract law, but it is incremental as it focuses on a specific domain and framework.
The paper tackled the problem of LLMs' limited effectiveness in legal reasoning due to specialized terminology and knowledge by introducing LegalSemi, a benchmark with 54 expert-annotated scenarios based on the IRAC framework in Malaysian Contract Law, and showed that incorporating a structured knowledge base improved IRAC analysis across four LLMs.
The effectiveness of Large Language Models (LLMs) in legal reasoning is often limited due to the unique legal terminologies and the necessity for highly specialized knowledge. These limitations highlight the need for high-quality data tailored for complex legal reasoning tasks. This paper introduces LegalSemi, a benchmark specifically curated for legal scenario analysis. LegalSemi comprises 54 legal scenarios, each rigorously annotated by legal experts, based on the comprehensive IRAC (Issue, Rule, Application, Conclusion) framework from Malaysian Contract Law. In addition, LegalSemi is accompanied by a structured knowledge base (SKE). A series of experiments were conducted to assess the usefulness of LegalSemi for IRAC analysis. The experimental results demonstrate the effectiveness of incorporating the SKE for issue identification, rule retrieval, application and conclusion generation using four different LLMs.