  While state-of-the-art large language models (LLMs) can excel at adapting
text from one style to another, current work does not address the
explainability of style transfer models. Recent work has explored generating
textual explanations from larger teacher models and distilling them into
smaller student models. One challenge with such approach is that LLM outputs
may contain errors that require expertise to correct, but gathering and
incorporating expert feedback is difficult due to cost and availability. To
address this challenge, we propose ICLEF, a novel human-AI collaboration
approach to model distillation that incorporates scarce expert human feedback
by combining in-context learning and model self-critique. We show that our
method leads to generation of high-quality synthetic explainable style transfer
datasets for formality (e-GYAFC) and subjective bias (e-WNC). Via automatic and
human evaluation, we show that specialized student models fine-tuned on our
datasets outperform generalist teacher models on the explainable style transfer
task in one-shot settings, and perform competitively compared to few-shot
teacher models, highlighting the quality of the data and the role of expert
feedback. In an extrinsic task of authorship attribution, we show that
explanations generated by smaller models fine-tuned on e-GYAFC are more
predictive of authorship than explanations generated by few-shot teacher
models.
