Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions
arXiv:2605.24452v1 Announce Type: new Abstract: Legal NLP benchmarks evaluate models on randomly split data, implicitly assuming that legal language is stationary. We test this assumption by fine-tuning four transformer encoders -- XLM-RoBERTa (base and large) and their legal-domain variants -- on Ukrainian court decisions from three temporal epochs defined by geopolitical disruptions: pre-war (2008-2013), hybrid war (2014-2021), and full-scale invasion (2022-2026). Each model is trained on one epoch and evaluated on all three, producing a 3x3 cross-temporal generalization matrix. Four finding
This research is emerging now as the AI community increasingly confronts the limitations of static NLP benchmarks in dynamic real-world environments, particularly in politically volatile contexts.
A strategic reader should care because the inability of AI models to adapt to temporal concept drift in critical domains like legal judgment undermines their reliability and utility, especially for high-stakes applications.
This research provides a framework for understanding how geopolitical events directly impact the performance of AI models, shifting focus from static model performance to adaptive robustness over time.
- · AI researchers in robustness
- · Legal tech developers
- · Ukrainian legal system (potentially)
- · Governments investing in adaptive AI
- · Developers of non-adaptive AI models
- · Organizations relying on static AI benchmarks
- · Legal systems in flux without adaptive AI
- · Users of inflexible legal AI tools
AI models for legal and political analysis will need to explicitly account for temporal concept drift.
This imperative will drive investment into R&D for adaptive learning, continuous fine-tuning, and robust change detection mechanisms in AI.
The successful implementation of such adaptive AI could lead to more resilient and trustworthy automated systems in governance, potentially influencing policy and legal interpretations in dynamic geopolitical landscapes.
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Read at arXiv cs.CL