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Existing reference-free metrics have obvious limitations for evaluatingcontrolled text generation models. Unsupervised metrics can only provide atask-agnostic evaluation result which correlates weakly with human judgments,whereas supervised ones may overfit task-specific data with poor generalizationability to other datasets. In this paper, we propose an unsupervisedreference-free metric called CTRLEval, which evaluates controlled textgeneration from different aspects by formulating each aspect into multiple textinfilling tasks. On top of these tasks, the metric assembles the generationprobabilities from a pre-trained language model without any model training.Experimental results show that our metric has higher correlations with humanjudgments than other baselines, while obtaining better generalization ofevaluating generated texts from different models and with different qualities.

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation