Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data

Abstract: We analyze the ability of pre-trained LLMs to transfer knowledge among datasets annotated with different type systems and to generalize beyond the domain and dataset they were trained on. We create a meta task, over multiple datasets focused on the prediction of rhetorical roles. Prediction of the rhetorical role a sentence plays in a case decision is an important and often studied task in AI & Law. Typically, it requires the annotation of a large number of sentences to train a model, which can be time-consuming and expensive. Further, the application of the models is restrained to the same dataset it was trained on. We fine-tune LLMs and evaluate their performance across datasets, to investigate the models' ability to generalize across domains. Our results suggest that the approach could be helpful in overcoming the cold-start problem in active or interactvie learning, and shows the ability of the models to generalize across datasets and domains.