Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
arXiv:2602.02304v2 Announce Type: replace-cross Abstract: Large-scale foundation models exhibit \emph{behavioral shifts} when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context learning. Current explainability methods are structurally ill-suited to explain these shifts, because they either treat models as static objects, as traditional eXplainable AI (XAI) approaches do, or merely compare independent explanations across different checkpoints of a model. As a result, these approaches fail to explain the functional transition betwee
This paper highlights the growing awareness within the AI community that current explainability methods are insufficient for understanding the dynamic behavior of large-scale foundation models, especially as they undergo various forms of intervention.
A strategic reader should care because the inability to explain behavioral shifts in LLMs impedes reliable development, deployment, and auditing, which is crucial for safety, trust, and effective integration into critical systems.
The focus of explainable AI is shifting from static model analysis to understanding and explaining the dynamic evolutionary processes and 'behavioral shifts' of AI models.
- · AI safety and ethics researchers
- · Developers of new explainability techniques
- · Auditors and regulators of AI systems
- · Enterprises deploying adaptive AI
- · Traditional XAI approaches
- · Developers neglecting behavioral shifts
- · Users distrustful of opaque AI
- · Organizations relying on static model understanding
New research and tooling will emerge to address dynamic explainability in LLMs.
This will lead to more robust, auditable, and trustworthy AI systems, accelerating their responsible adoption in sensitive domains.
Increased transparency into AI's evolving behavior could influence future regulatory frameworks and public perception of autonomous systems.
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Read at arXiv cs.LG