Controlled Text Generation for Black-box Language Models via Score-based
  Progressive Editor
Sangwon Yu Changmin Lee Hojin Lee Sungroh Yoon
Despite recent progress in language models, generating constrained text for
specific domains remains a challenge, particularly when utilizing black-box
models that lack domain-specific knowledge. In this paper, we introduce ScoPE
(Score-based Progressive Editor) generation, a novel approach for controlled
text generation for black-box language models. We employ ScoPE to facilitate
text generation in the target domain by integrating it with language models
through a cascading approach. Trained to enhance the target domain score of the
edited text, ScoPE progressively edits intermediate output discrete tokens to
align with the target attributes throughout the auto-regressive generation
process of the language model. This iterative process guides subsequent steps
to produce desired output texts for the target domain. Our experimental results
on diverse controlled generations demonstrate that ScoPE effectively
facilitates controlled text generation for black-box language models in both
in-domain and out-of-domain conditions, which is challenging for existing
methods.