  In recent years, large-scale transformer decoders such as the GPT-x family of
models have become increasingly popular. Studies examining the behavior of
these models tend to focus only on the output of the language modeling head and
avoid analysis of the internal states of the transformer decoder. In this
study, we present a collection of methods to analyze the hidden states of GPT-2
and use the model's navigation of garden path sentences as a case study. To
enable this, we compile the largest currently available dataset of garden path
sentences. We show that Manhattan distances and cosine similarities provide
more reliable insights compared to established surprisal methods that analyze
next-token probabilities computed by a language modeling head. Using these
methods, we find that negating tokens have minimal impacts on the model's
representations for unambiguous forms of sentences with ambiguity solely over
what the object of a verb is, but have a more substantial impact of
representations for unambiguous sentences whose ambiguity would stem from the
voice of a verb. Further, we find that analyzing the decoder model's hidden
states reveals periods of ambiguity that might conclude in a garden path effect
but happen not to, whereas surprisal analyses routinely miss this detail.
