Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
This paper proposes casting multi-label legal annotation as a retrieval task, using a frozen retrieval model to embed documents and label descriptions, then predicting labels via k-nearest neighbors. Experiments on three legal datasets show competitive accuracy, high data efficiency, and zero hallucination compared to generative models.
[2605.16767] Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
[Submitted on 16 May 2026]
Title:Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
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Abstract:Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited supervision. Parametric encoders typically require task-specific training and retraining when the label set changes, while prompting generative large language models becomes costly and degrades as the label space grows. We cast legal annotation as retrieval: we embed documents and label descriptions with a frozen retrieval model and predict labels via k-nearest neighbors in the embedding space, enabling updates by re-embedding and re-indexing rather than gradient-based backpropagation. Across three legal datasets (ECtHR-A, ECtHR-B, and Eurlex with 100 labels), retrieval achieves competitive accuracy and strong data efficiency; on Eurlex, Qwen-8B retrieval improves Macro-F1 from 40.41 (GPT-5.2, zero-shot) to 49.12 while reducing estimated compute by 20-30 times compared to fine-tuning. With only (N=100) training samples, retrieval nearly doubles Micro-F1 over hierarchical Legal-BERT on ECtHR-A (48.29 vs. 27.87). We also quantify a reliability failure mode of generative inference: GPT-5.2 hallucinates labels outside the provided taxonomy in 0.12-0.9% of test samples under deterministic decoding. In contrast, retrieval strictly respects defined label sets, eliminating hallucination by design. These results suggest retrieval-model-based annotators are a practical, deployable alternative for high-cardinality and rapidly changing legal label spaces.
Comments: 10 pages, 3 figures
Subjects:
Computation and Language (cs.CL)
MSC classes: 68T50
ACM classes: I.2.7; I.2.4
Cite as: arXiv:2605.16767 [cs.CL]
(or arXiv:2605.16767v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.16767
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Li Zhang [view email] [v1] Sat, 16 May 2026 02:40:01 UTC (411 KB)
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