Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free

The paper proposes casting multi-label legal annotation as a retrieval task, using frozen models and k-nearest neighbors to assign labels. This method achieves competitive accuracy and strong data efficiency across legal datasets, significantly reducing computational costs compared to fine-tuning large language models.