Legal Standards Extraction Using LLMs with CRF-based Sequence Labeling
Keywords:
legal standards extraction, conditional random fields, sequence labeling, transformer embeddings, BERT-CRF, RoBERTa-CRFAbstract
In complicated legal documents like GDPR and Dodd-Frank, LLM token embeddings and CRFs extract compliance references. Over transformer-only baselines, sequential dependency modeling and transformer-based designs like BERT and RoBERTa with contextualized embeddings increase legal standards extraction accuracy and recall. Complex legal concepts, multi-entity linkages, and nested phrase structures are assessed on annotated regulatory texts using CRF-augmented models. Laws are needed for e-discovery, regulatory compliance audits, and worldwide law firm risk assessment. In sequence labeling, sentence boundary recognition, and legal language adaption, CRF-enhanced models thrive. We found hybrid LLM-CRF systems automate legal information extraction and enhance compliance-driven decision-making.
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References
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