Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models

David Wingate, Mohammad Shoeybi, Taylor Sorensen

Abstract:

We explore the idea of compressing the
prompts used to condition language models,
and show that compressed prompts can re-
tain a substantive amount of information about
the original prompt. For severely compressed
prompts, while fine-grained information is
lost, abstract information and general senti-
ments can be retained with surprisingly few pa-
rameters, which can be useful in the context
of decode-time algorithms for controllability
and toxicity reduction. We explore contrastive
conditioning to steer language model gener-
ation towards desirable text and away from
undesirable text, and find that some complex
prompts can be effectively compressed into a
single token to guide generation. We also show
that compressed prompts are largely composi-
tional, and can be constructed such that they
can be used to control independent aspects of
generated text.