@inproceedings{a3ff31d3a6ae47dea98e1d5ccf19ed51,
title = "NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus",
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.",
author = "Kyoungyeon Cho and Seungkum Han and Choi, {Young Rok} and Wonseok Hwang",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 ; Conference date: 17-03-2024 Through 22-03-2024",
year = "2024",
language = "English",
series = "EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations",
publisher = "Association for Computational Linguistics (ACL)",
pages = "52--61",
editor = "Nikolaos Aletras and {De Clercq}, Orphee",
booktitle = "EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations",
address = "United States",
}