Probabilistic Attribution For Large Language Models

This work uses the conditional probabilities computed by LLMs to situate them within the mathematical theory of stochastic processes. It presents a model-agnostic probabilistic token attribution measure, using Bayes' rule to capture the model's internal representation of the distribution over token sequences.