Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CTRLEval: An Unsupervised Reference-Free Metric for Evaluating
  Controlled Text Generation   Existing reference-free metrics have obvious limitations for evaluating
controlled text generation models. Unsupervised metrics can only provide a
task-agnostic evaluation result which correlates weakly with human judgments,
whereas supervised ones may overfit task-specific data with poor generalization
ability to other datasets. In this paper, we propose an unsupervised
reference-free metric called CTRLEval, which evaluates controlled text
generation from different aspects by formulating each aspect into multiple text
infilling tasks. On top of these tasks, the metric assembles the generation
probabilities from a pre-trained language model without any model training.
Experimental results show that our metric has higher correlations with human
judgments than other baselines, while obtaining better generalization of
evaluating generated texts from different models and with different qualities.

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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph