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This study uses language model embeddings to quantify semantic association in self-paced reading and EEG data, examining how different implementations affect measures of reading difficulty.
This paper tests the Parse Multiplicity Mismatch Hypothesis, proposing that language models underpredict human processing difficulty in garden path sentences because they can consider more simultaneous parses. Using RNNGs with beam search, they find reducing the number of active parses increases predicted garden path effects, but not enough to fully capture human data.