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.

提出通过向 ChatGPT 模型蒸馏来改进形式转换数据集的解释性，进一步使用以上下文学习为基础的 ChatGPT 作为评论者来整合稀缺的专家人工反馈以提炼生成的解释。使用此数据集表明当前分发的模型在形式转换任务上表现不佳，而在我们的高质量数据集上进行微调可以显著改进，同时在人类评估中显示出比 ChatGPT 小得多的模型更好地与专家偏好一致。最后，讨论了在解释性的形式转换任务上进行微调的模型的两个潜在应用：可解释的作者验证和对 AI 生成文本检测器的可解释的对抗攻击。