  We propose CHRT (Control Hidden Representation Transformation) - a controlled
language generation framework that steers large language models to generate
text pertaining to certain attributes (such as toxicity). CHRT gains attribute
control by modifying the hidden representation of the base model through
learned transformations. We employ a contrastive-learning framework to learn
these transformations that can be combined to gain multi-attribute control. The
effectiveness of CHRT is experimentally shown by comparing it with seven
baselines over three attributes. CHRT outperforms all the baselines in the task
of detoxification, positive sentiment steering, and text simplification while
minimizing the loss in linguistic qualities. Further, our approach has the
lowest inference latency of only 0.01 seconds more than the base model, making
it the most suitable for high-performance production environments. We
open-source our code and release two novel datasets to further propel
controlled language generation research.
