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ACL-Verbatim introduces a family of lightweight extractive models for grounded RAG that return exact text spans from source, outperforming larger LLM-based extractors.
This paper proposes a retrieval-based approach for multi-label legal annotation that uses frozen embedding models to retrieve labels via k-nearest neighbors, achieving competitive accuracy, high data efficiency, and eliminating label hallucination by design.