Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Higher-order Comparisons of Sentence Encoder Representations   Representational Similarity Analysis (RSA) is a technique developed by
neuroscientists for comparing activity patterns of different measurement
modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has
several advantages over existing approaches to interpretation of language
encoders based on probing or diagnostic classification: namely, it does not
require large training samples, is not prone to overfitting, and it enables a
more transparent comparison between the representational geometries of
different models and modalities. We demonstrate the utility of RSA by
establishing a previously unknown correspondence between widely-employed
pretrained language encoders and human processing difficulty via eye-tracking
data, showcasing its potential in the interpretability toolbox for neural
models

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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: eess