Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 26·5 min read
Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions
TL;DR
Legal NLP models often assume that legal language remains constant over time, which this study challenges. Four transformer encoders, including XLM-RoBERTa, were fine-tuned on Ukrainian court decisions from three distinct geopolitical periods.
✦ Why It Matters
Engineers can leverage insights on temporal concept drift to improve legal NLP model robustness over time.
Key Takeaways
- Consider temporal changes in legal language when developing NLP models.
- Utilize transformer encoders like XLM-RoBERTa for legal judgment tasks.
- Implement continual learning strategies to retain knowledge across epochs.
- Access the publicly available dataset for training and evaluation.
Full Summary
Legal Natural Language Processing (NLP) benchmarks typically evaluate models on randomly split datasets, assuming that the language used in legal contexts is stable. This research challenges that assumption by examining how legal language evolves over time, specifically through the lens of Ukrainian court decisions across three geopolitical epochs: pre-war (2008-2013), hybrid war (2014-2021), and full-scale invasion (2022-2026).
Four transformer encoders, including XLM-RoBERTa and its legal-domain variant, were fine-tuned on these datasets. The study found that models trained on pre-war data experienced a severe drop in performance (up to 27.2 percentage points in macro-F1) when applied to decisions from the full-scale invasion era.
Additionally, backward transfer from later to earlier epochs was more robust, suggesting that legal language builds upon itself. Legal-domain pretraining reduced the magnitude of performance degradation but did not improve overall accuracy.
The dataset, comprising 428,000 decisions, is publicly available, providing a resource for further research.