  This study investigates the challenges posed by the dynamic nature of legal
multi-label text classification tasks, where legal concepts evolve over time.
Existing models often overlook the temporal dimension in their training
process, leading to suboptimal performance of those models over time, as they
treat training data as a single homogeneous block. To address this, we
introduce ChronosLex, an incremental training paradigm that trains models on
chronological splits, preserving the temporal order of the data. However, this
incremental approach raises concerns about overfitting to recent data,
prompting an assessment of mitigation strategies using continual learning and
temporal invariant methods. Our experimental results over six legal multi-label
text classification datasets reveal that continual learning methods prove
effective in preventing overfitting thereby enhancing temporal
generalizability, while temporal invariant methods struggle to capture these
dynamics of temporal shifts.
