  While there is much recent interest in studying why Transformer-based large
language models make predictions the way they do, the complex computations
performed within each layer have made their behavior somewhat opaque. To
mitigate this opacity, this work presents a linear decomposition of final
hidden states from autoregressive language models based on each initial input
token, which is exact for virtually all contemporary Transformer architectures.
This decomposition allows the definition of probability distributions that
ablate the contribution of specific input tokens, which can be used to analyze
their influence on model probabilities over a sequence of upcoming words with
only one forward pass from the model. Using the change in next-word probability
as a measure of importance, this work first examines which context words make
the biggest contribution to language model predictions. Regression experiments
suggest that Transformer-based language models rely primarily on collocational
associations, followed by linguistic factors such as syntactic dependencies and
coreference relationships in making next-word predictions. Additionally,
analyses using these measures to predict syntactic dependencies and coreferent
mention spans show that collocational association and repetitions of the same
token largely explain the language models' predictions on these tasks.
