  Multi-Label Classification (MLC) is a common task in the legal domain, where
more than one label may be assigned to a legal document. A wide range of
methods can be applied, ranging from traditional ML approaches to the latest
Transformer-based architectures. In this work, we perform an evaluation of
different MLC methods using two public legal datasets, POSTURE50K and
EURLEX57K. By varying the amount of training data and the number of labels, we
explore the comparative advantage offered by different approaches in relation
to the dataset properties. Our findings highlight DistilRoBERTa and LegalBERT
as performing consistently well in legal MLC with reasonable computational
demands. T5 also demonstrates comparable performance while offering advantages
as a generative model in the presence of changing label sets. Finally, we show
that the CrossEncoder exhibits potential for notable macro-F1 score
improvements, albeit with increased computational costs.
