  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.
