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arXiv · 2605.17691

Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification

M. Mikail Demir·M. Abdullah Canbaz
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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.

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M. Mikail Demir, M. Abdullah Canbaz. 2026-05-17. Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification. https://doi.org/10.18653/v1%2F2025.nllp-1.13

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