Large Language Models are legal but they are not: Making the case for a powerful LegalLLM

TL;DR

This paper evaluates the zero-shot legal classification performance of general-purpose LLMs compared to specialized legal models, highlighting the need for more powerful LegalLLMs due to performance gaps.

Contribution

It provides a comparative analysis of general LLMs versus legal-domain models on legal classification tasks, emphasizing the importance of domain-specific training.

Findings

General LLMs perform reasonably well without legal training

Legal-domain models outperform general LLMs by up to 19.2/26.8% in F1 scores

Domain-specific LLMs are crucial for improved legal NLP performance

Abstract

Realizing the recent advances in Natural Language Processing (NLP) to the legal sector poses challenging problems such as extremely long sequence lengths, specialized vocabulary that is usually only understood by legal professionals, and high amounts of data imbalance. The recent surge of Large Language Models (LLMs) has begun to provide new opportunities to apply NLP in the legal domain due to their ability to handle lengthy, complex sequences. Moreover, the emergence of domain-specific LLMs has displayed extremely promising results on various tasks. In this study, we aim to quantify how general LLMs perform in comparison to legal-domain models (be it an LLM or otherwise). Specifically, we compare the zero-shot performance of three general-purpose LLMs (ChatGPT-20b, LLaMA-2-70b, and Falcon-180b) on the LEDGAR subset of the LexGLUE benchmark for contract provision classification. Although the LLMs were not explicitly trained on legal data, we observe that they are still able to classify the theme correctly in most cases. However, we find that their mic-F1/mac-F1 performance is up to 19.2/26.8\% lesser than smaller models fine-tuned on the legal domain, thus underscoring the need for more powerful legal-domain LLMs.