  How to usefully encode compositional task structure has long been a core
challenge in AI. Recent work in chain of thought prompting has shown that for
very large neural language models (LMs), explicitly demonstrating the
inferential steps involved in a target task may improve performance over
end-to-end learning that focuses on the target task alone. However, chain of
thought prompting has significant limitations due to its dependency on huge
pretrained LMs. In this work, we present compositional fine-tuning (CFT): an
approach based on explicitly decomposing a target task into component tasks,
and then fine-tuning smaller LMs on a curriculum of such component tasks. We
apply CFT to recommendation tasks in two domains, world travel and local
dining, as well as a previously studied inferential task (sports
understanding). We show that CFT outperforms end-to-end learning even with
equal amounts of data, and gets consistently better as more component tasks are
modeled via fine-tuning. Compared with chain of thought prompting, CFT performs
at least as well using LMs only 7.4% of the size, and is moreover applicable to
task domains for which data are not available during pretraining.
