Large Language Models are legal but they are not: Making the case for a powerful LegalLLM
This work addresses the problem of applying NLP to the legal domain for tasks like contract classification, but it is incremental as it primarily benchmarks existing models without introducing new methods.
The study compared general-purpose large language models (LLMs) like ChatGPT-20b, LLaMA-2-70b, and Falcon-180b with legal-domain models on contract provision classification using the LEDGAR benchmark, finding that general LLMs performed up to 19.2% (mic-F1) and 26.8% (mac-F1) worse than fine-tuned legal models, highlighting a performance gap.
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.