Augmenting Legal Decision Support Systems with LLM-based NLI for Analyzing Social Media Evidence

TL;DR

The system emerged as the winning submission for NLLP 2024 shared task on Legal Natural Language Inference, significantly outperforming other entries with a substantial margin and demonstrating the effectiveness of the approach in legal text analysis.

Abstract

This paper presents our system description and error analysis of our entry for NLLP 2024 shared task on Legal Natural Language Inference (L-NLI) \citep{hagag2024legallenssharedtask2024}. The task required classifying these relationships as entailed, contradicted, or neutral, indicating any association between the review and the complaint. Our system emerged as the winning submission, significantly outperforming other entries with a substantial margin and demonstrating the effectiveness of our approach in legal text analysis. We provide a detailed analysis of the strengths and limitations of each model and approach tested, along with a thorough error analysis and suggestions for future improvements. This paper aims to contribute to the growing field of legal NLP by offering insights into advanced techniques for natural language inference in legal contexts, making it accessible to both experts and newcomers in the field.

Figures (15)

• Figure 1: Multi-stage Training Overview
• Figure 2: Confusion Matrix : Our system's (best) predictions over the test set
• Figure 3: Confusion Matrix : Our system's (submission) predictions over the test set
• Figure 4: performance on test set : GEMMA2-SNLI : BIPA
• Figure 5: performance on test set : GEMMA2-SNLI : Consumer