Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
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 co...
| Authors | M. Mikail Demir, M. Abdullah Canbaz |
|---|---|
| Primary category | cs.CL |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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