  In this paper, we introduce CuSINeS, a negative sampling approach to enhance
the performance of Statutory Article Retrieval (SAR). CuSINeS offers three key
contributions. Firstly, it employs a curriculum-based negative sampling
strategy guiding the model to focus on easier negatives initially and
progressively tackle more difficult ones. Secondly, it leverages the
hierarchical and sequential information derived from the structural
organization of statutes to evaluate the difficulty of samples. Lastly, it
introduces a dynamic semantic difficulty assessment using the being-trained
model itself, surpassing conventional static methods like BM25, adapting the
negatives to the model's evolving competence. Experimental results on a
real-world expert-annotated SAR dataset validate the effectiveness of CuSINeS
across four different baselines, demonstrating its versatility.
