Empirical Evidence of Interpretation Drift & Taxonomy Field Guide
Published:Dec 28, 2025 21:36
•1 min read
•r/learnmachinelearning
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 a temperature setting of 0. The author argues that this issue is often dismissed but is a significant problem 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 or accuracy debates. The goal is to help practitioners recognize and address this issue in their daily work.
Key Takeaways
- •Interpretation Drift is a significant, often overlooked problem in LLMs.
- •It manifests as inconsistent interpretations of the same input over time or across models.
- •The Interpretation Drift Taxonomy aims to provide a shared language for discussing and addressing this issue.
Reference
“"The real failure mode isn’t bad outputs, it’s this drift hiding behind fluent responses."”