Researcher Struggles to Explain Interpretation Drift in LLMs
Published:Dec 25, 2025 09:31
•1 min read
•r/mlops
Analysis
The article highlights a critical issue in LLM research: interpretation drift. The author is attempting to study how LLMs interpret tasks and how those interpretations change over time, leading to inconsistent outputs even with identical prompts. The core problem is that reviewers are focusing on superficial solutions like temperature adjustments and prompt engineering, which can enforce consistency but don't guarantee accuracy. The author's frustration stems from the fact that these solutions don't address the underlying issue of the model's understanding of the task. The example of healthcare diagnosis clearly illustrates the problem: consistent, but incorrect, answers are worse than inconsistent ones that might occasionally be right. The author seeks advice on how to steer the conversation towards the core problem of interpretation drift.
Key Takeaways
- •LLMs can exhibit interpretation drift, leading to inconsistent outputs even with identical prompts.
- •Focusing solely on temperature and prompt engineering can mask the underlying issue of model understanding.
- •Ensuring consistency without accuracy is not a desirable outcome, especially in critical applications like healthcare.
Reference
““What I’m trying to study isn’t randomness, it’s more about how models interpret a task and how it changes what it thinks the task is from day to day.””