Empirical Evidence Of Interpretation Drift & Taxonomy Field Guide
Analysis
This article discusses the phenomenon of "Interpretation Drift" in Large Language Models (LLMs), where the model's interpretation of the same input changes over time or across different models, even with identical prompts. The author argues that this drift is often dismissed but is a significant issue in MLOps pipelines, leading to unstable AI-assisted decisions. The article introduces an "Interpretation Drift Taxonomy" to build a shared language and understanding around this subtle failure mode, focusing on real-world examples rather than benchmarking accuracy. The goal is to help practitioners recognize and address this problem in their AI systems, shifting the focus from output acceptability to interpretation stability.
Key Takeaways
- •Interpretation Drift is a significant, often overlooked problem in LLMs.
- •A shared language and taxonomy are needed to address this issue effectively.
- •Focus should shift from output acceptability to interpretation stability.
“"The real failure mode isn’t bad outputs, it’s this drift hiding behind fluent responses."”