Interpretation Drift
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Understanding Interpretation Drift in AI Systems
Read Full Article: Understanding Interpretation Drift in AI Systems
Interpretation Drift in large language models (LLMs) is often overlooked, dismissed as mere stochasticity or a solved issue, yet it poses significant challenges in AI-assisted decision-making. This phenomenon is not about bad outputs but about the instability of interpretations across different runs or over time, which can lead to inconsistent AI behavior. A new Interpretation Drift Taxonomy aims to create a shared language and understanding of this subtle failure mode by collecting real-world examples, helping those in the field recognize and address these issues. This matters because stable and reliable AI outputs are crucial for effective decision-making and trust in AI systems.
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