ICLEF: In-Context Learning with Expert Feedback for Explainable Style
  Transfer
Arkadiy Saakyan Smaranda Muresan
While state-of-the-art language models excel at the style transfer task,
current work does not address explainability of style transfer systems.
Explanations could be generated using large language models such as GPT-3.5 and
GPT-4, but the use of such complex systems is inefficient when smaller, widely
distributed, and transparent alternatives are available. We propose a framework
to augment and improve a formality style transfer dataset with explanations via
model distillation from ChatGPT. To further refine the generated explanations,
we propose a novel way to incorporate scarce expert human feedback using
in-context learning (ICLEF: In-Context Learning from Expert Feedback) by
prompting ChatGPT to act as a critic to its own outputs. We use the resulting
dataset of 9,960 explainable formality style transfer instances (e-GYAFC) to
show that current openly distributed instruction-tuned models (and, in some
settings, ChatGPT) perform poorly on the task, and that fine-tuning on our
high-quality dataset leads to significant improvements as shown by automatic
evaluation. In human evaluation, we show that models much smaller than ChatGPT
fine-tuned on our data align better with expert preferences. Finally, we
discuss two potential applications of models fine-tuned on the explainable
style transfer task: interpretable authorship verification and interpretable
adversarial attacks on AI-generated text detectors.