Tracing Model Outputs to the Training Data

This post introduces an efficient method for scaling influence functions to large language models (up to 52 billion parameters) to understand how training data influences model outputs. It observes that generalization patterns become more abstract with model scale and that influence is distributed across layers but can be localized. The research connects these findings to mechanistic interpretability, showing how data repetition can damage generalization structures.