Legal Prompt Engineering for Multilingual Legal Judgement Prediction

TL;DR

This paper explores the use of zero-shot legal prompt engineering with large language models to predict legal judgments from long documents across multiple languages, demonstrating transferability without domain-specific training.

Contribution

It shows that zero-shot LPE can outperform baselines in legal judgment prediction across multiple languages without domain-specific data or fine-tuning.

Findings

Zero-shot LPE outperforms baseline methods.

Transfer to legal domain is feasible without domain-specific data.

No additional training or fine-tuning required.

Abstract

Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal Judgement Prediction (LJP) task. We investigate the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights (in English) and the Federal Supreme Court of Switzerland (in German, French and Italian). Our results show that zero-shot LPE is better compared to the baselines, but it still falls short compared to current state of the art supervised approaches. Nevertheless, the results are important, since there was 1) no explicit domain-specific data used - so we show that the transfer to the legal domain is possible for general-purpose LLMs, and 2) the LLMs where directly applied without any further training or fine-tuning - which in turn saves immensely in terms of additional computational costs.