LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents

The rise of Large Language Models (LLMs) has had a profoundly transformative effect on a number of fields and domains. However, their uptake in Law has proven more challenging due to the important issues of reliability and transparency. In this study, we present a structured prompting methodology as a viable alternative to the often expensive fine-tuning, with the capability of tacking long legal documents from the CUAD dataset on the task of information retrieval. Each document is first split into chunks via a system of chunking and augmentation, addressing the long document problem. Then, alongside an engineered prompt, the input is fed into QWEN-2 to produce a set of answers for each question. Finally, we tackle the resulting candidate selection problem with the introduction of the Distribution-based Localisation and Inverse Cardinality Weighting heuristics. This approach leverages a general purpose model to promote long term scalability, prompt engineering to increase reliability and the two heuristic strategies to reduce the impact of the black box effect. Whilst our model performs up to 9% better than the previously presented method, reaching state-of-the-art performance, it also highlights the limiting factor of current automatic evaluation metrics for question answering, serving as a call to action for future research. However, the chief aim of this work is to underscore the potential of structured prompt engineering as a useful, yet under-explored, tool in ensuring accountability and responsibility of AI in the legal domain, and beyond.