EleutherAI

This analysis enables precise measurement of token-level sensitivity and probability distribution shifts. It allows researchers to calculate KL divergence and log-probability metrics during the prefill phase, providing granular insights into how specific input tokens influence the internal state and subsequent output variance of neural architectures.

These capabilities are designed for research scientists, machine learning engineers, and model evaluators focused on interpretability and performance benchmarking. Professionals working on alignment, robustness testing, and distribution shift detection will find these metrics essential for rigorous quantitative assessment of model behavior.

Execution requires access to the model's internal log-probability outputs and the ability to process prefill sequences. Users must have a configured environment capable of handling high-dimensional tensor data and the specific mathematical libraries required to compute KL divergence and sensitivity distributions from raw model logits.