Interpretable Long-Form Legal Question Answering with
  Retrieval-Augmented Large Language Models
Antoine Louis Gijs van Dijck Gerasimos Spanakis
Many individuals are likely to face a legal dispute at some point in their
lives, but their lack of understanding of how to navigate these complex issues
often renders them vulnerable. The advancement of natural language processing
opens new avenues for bridging this legal literacy gap through the development
of automated legal aid systems. However, existing legal question answering
(LQA) approaches often suffer from a narrow scope, being either confined to
specific legal domains or limited to brief, uninformative responses. In this
work, we propose an end-to-end methodology designed to generate long-form
answers to any statutory law questions, utilizing a "retrieve-then-read"
pipeline. To support this approach, we introduce and release the Long-form
Legal Question Answering (LLeQA) dataset, comprising 1,868 expert-annotated
legal questions in the French language, complete with detailed answers rooted
in pertinent legal provisions. Our experimental results demonstrate promising
performance on automatic evaluation metrics, but a qualitative analysis
uncovers areas for refinement. As one of the only comprehensive,
expert-annotated long-form LQA dataset, LLeQA has the potential to not only
accelerate research towards resolving a significant real-world issue, but also
act as a rigorous benchmark for evaluating NLP models in specialized domains.
We publicly release our code, data, and models.