  In recent years, there has been a growing interest in the development of
language models capable of generating text with controllable attributes. While
several approaches have been proposed, many of these methods require
condition-specific data or significant computational resources. In this study,
we propose a novel method called Gamma Sampling, which enables controllable
language generation without the need for any training data and maintains a fast
generation speed. Gamma Sampling incorporates attribute-related information
into the sampling process, effectively guiding the language model to produce
text with desired attributes. Our experimental results demonstrate that Gamma
Sampling, when applied to GPT2, outperforms representative baselines in terms
of diversity, attribute relevance, and overall quality of the generated
samples.
