  In the rapidly evolving field of text generation, the demand for more precise
control mechanisms has become increasingly apparent. To address this need, we
present a novel methodology, LIFI, which offers a lightweight approach with
fine-grained control for controlled text generation. Unlike previous studies
that train pre-trained language models to follow discrete, categorical, and
exclusive control codes, LIFI learns controlled text generation under the
guidance of continuous, relative, and nonexclusive control codes. These
fine-grained codes are automatically derived from an attribute classifier,
initially trained with a small amount of labeled data and subsequently employed
to label abundant unlabeled data, thus garnering more extensive supervision
signals. Moreover, to achieve efficient control, we incorporate the
fine-grained control codes with adapters, a parameter- and compute-efficient
way to steer a pre-trained language model. We evaluate LIFI on two conventional
tasks -- sentiment control and topic control -- and one newly proposed task --
stylistic novel writing. Comprehensive experimental results validate the
effectiveness of our proposed methods, demonstrating substantial performance
improvements over existing baselines.
