Legal Prompt Engineering for Multilingual Legal Judgement Prediction
This work addresses the challenge of automating legal judgement prediction for courts like the European Court of Human Rights and Swiss Federal Supreme Court, offering a computationally efficient approach, though it is incremental compared to existing supervised methods.
The paper tackled the problem of predicting legal judgements from multilingual case texts using zero-shot legal prompt engineering with large language models, showing it outperforms baselines but falls short of supervised state-of-the-art methods, with results indicating transfer to the legal domain without domain-specific data or fine-tuning.
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.