NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus

TL;DR

NESTLE is a no-code tool that leverages LLMs and custom IE systems to enable flexible, large-scale statistical analysis of legal corpora without programming, validated on Korean legal tasks.

Contribution

It introduces a comprehensive no-code platform combining LLMs and custom IE for customizable legal corpus analysis, filling a gap in existing tools.

Findings

Achieves GPT-4 level performance with minimal labeled data.

Successfully applied to Korean legal tasks.

Enables unlimited customizable analysis without coding.

Abstract

The statistical analysis of large scale legal corpus can provide valuable legal insights. For such analysis one needs to (1) select a subset of the corpus using document retrieval tools, (2) structure text using information extraction (IE) systems, and (3) visualize the data for the statistical analysis. Each process demands either specialized tools or programming skills whereas no comprehensive unified "no-code" tools have been available. Here we provide NESTLE, a no-code tool for large-scale statistical analysis of legal corpus. Powered by a Large Language Model (LLM) and the internal custom end-to-end IE system, NESTLE can extract any type of information that has not been predefined in the IE system opening up the possibility of unlimited customizable statistical analysis of the corpus without writing a single line of code. We validate our system on 15 Korean precedent IE tasks and 3 legal text classification tasks from LexGLUE. The comprehensive experiments reveal NESTLE can achieve GPT-4 comparable performance by training the internal IE module with 4 human-labeled, and 192 LLM-labeled examples.