Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
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Computer Science > Computation and Language
Title:Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
Abstract:Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant risk. To address the shortcomings of standard accuracy, this paper introduces a more robust evaluation framework. We benchmark modern Large Language Models on a new, expert-annotated dataset of 239 real-world legal citations and propose a novel Average Severity Error metric to better measure the practical impact of classification errors. Our experiments reveal a performance split. Google's Gemini 2.5 Flash achieved the highest accuracy on a high-level classification task (79.1%), while OpenAI's GPT-5-mini was the top performer on the more complex fine-grained schema (67.7%). This work establishes a crucial baseline, provides a new context-rich dataset, and introduces an evaluation metric tailored to the demands of this complex legal reasoning task.
| Comments: | Accepted for publication at the Natural Legal Language Processing Workshop (NLLP) 2025, co-located with EMNLP |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.17691 [cs.CL] |
| (or arXiv:2605.17691v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.17691
arXiv-issued DOI via DataCite (pending registration)
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| Related DOI: | https://doi.org/10.18653/v1/2025.nllp-1.13
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