mlsanityData to 14 Oct 2026

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

arXiv 2309.04146 ↗First posted 8 Sep 2023cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

22 of 39 references.

References · 22

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  4. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  5. 2023AGIEval: A Human-Centric Benchmark for Evaluating Foundation Modelscited by 716
  6. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Casescited by 393
  7. 2021ZeRO-Offload: Democratizing Billion-Scale Model Trainingcited by 341
  8. 2021LexGLUE: A Benchmark Dataset for Legal Language Understanding in Englishcited by 310
  9. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 274
  10. 2021CUAD: An Expert-Annotated NLP Dataset for Legal Contract Reviewcited by 236
  11. 2022Is GPT-3 a Good Data Annotator?cited by 208
  12. 2023AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotatorscited by 172
10 more
  1. 2023Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!cited by 119
  2. 2023Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflowcited by 76
  3. 2018CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Servicecited by 73
  4. 2022A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Predictioncited by 44
  5. 2023Prompt2Model: Generating Deployable Models from Natural Language Instructionscited by 41
  6. 2022LEVEN: A Large-Scale Chinese Legal Event Detection Datasetcited by 36
  7. 2022Mining Legal Arguments in Court Decisionscited by 35
  8. 2023ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmarkcited by 33
  9. 2022Data-efficient End-to-end Information Extraction for Statistical Legal Analysiscited by 3
  10. 2022Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documentscited by 2

Built on by

Not cited yet.