The End of the Train-Test Split
7 days ago
- #LLM-challenges
- #content-moderation
- #machine-learning
- Building a butt classification model at Facebook involves training a CNN for edge detection with high precision and recall.
- Policy team requests a more context-aware model for sexually suggestive content, leading to challenges with LLM decision trees and lower accuracy.
- Discrepancies in labels and policy ambiguity cause issues, with outsourced labelers struggling with nuanced definitions like 'sexually suggestive'.
- Expert input is crucial for complex tasks, but their limited availability makes large labeled datasets difficult to maintain.
- LLMs require clear natural language rules and examples rather than traditional training sets, shifting focus from hyperparameter tuning to policy alignment.
- High error rates in 'golden sets' and expert disagreement highlight the need for continuous feedback loops between policy and engineering teams.
- Traditional train-test splits fail for complex LLM tasks due to label ambiguity and the need for expert review of model explanations.
- Shadow mode testing and direct communication between teams are essential for resolving edge cases and improving model accuracy.
- LLMs excel at enforcing natural language rules but require rigorous policy alignment and ongoing evaluation to handle complex classifications.
- The future of LLMs in tasks like legal or content moderation depends on solving alignment challenges and improving model self-awareness of errors.