Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training
To bridge this gap, we present a multi-task pre-training approach for German traffic law (StVO).
ProofPaper ↗
Key points
- Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target this structural capability.
- Our approach combines three complementary objectives: Next Sentence Prediction (NSP), Knowledge Graph (KG) Guided Clustering (CLU), and Knowledge Graph (KG) Aware Paragraph Classification (CLS).
- This approach explicitly encodes structured legal knowledge directly into the model weights.
- This encoding enables zero-shot legal reasoning at inference time and removes the need for discrete graph traversal.
Sources (1)
- [1]Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-trainingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 03:21 PM
To bridge this gap, we present a multi-task pre-training approach for German traffic law (StVO).
Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target this structural capability.
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