Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target this structural capability. A new arXiv paper, Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training, takes on this limitation.
The abstract notes that current legal language models rely solely on—the sentence is cut off in the available text—but the paper's title indicates the proposed remedy: a knowledge graph-guided multi-task pre-training framework. The idea is to inject structural knowledge during pre-training rather than leaving the model to infer document structure incidentally.
Because the abstract is truncated, no experimental results or comparisons are available in the source. The contribution, as stated, is the identification of a missing capability and a training strategy designed to address it.