Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning with Latent Language   The named concepts and compositional operators present in natural language
provide a rich source of information about the kinds of abstractions humans use
to navigate the world. Can this linguistic background knowledge improve the
generality and efficiency of learned classifiers and control policies? This
paper aims to show that using the space of natural language strings as a
parameter space is an effective way to capture natural task structure. In a
pretraining phase, we learn a language interpretation model that transforms
inputs (e.g. images) into outputs (e.g. labels) given natural language
descriptions. To learn a new concept (e.g. a classifier), we search directly in
the space of descriptions to minimize the interpreter's loss on training
examples. Crucially, our models do not require language data to learn these
concepts: language is used only in pretraining to impose structure on
subsequent learning. Results on image classification, text editing, and
reinforcement learning show that, in all settings, models with a linguistic
parameterization outperform those without.

28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex