Large Language Models (LLMs) have demonstrated impressive capabilities for
text rewriting. Nonetheless, the large sizes of these models make them
impractical for on-device inference, which would otherwise allow for enhanced
privacy and economical inference. Creating a smaller yet potent language model
for text rewriting presents a formidable challenge because it requires
balancing the need for a small size with the need to retain the emergent
capabilities of the LLM, that requires costly data collection. To address the
above challenge, we introduce a new instruction tuning approach for building a
mobile-centric text rewriting model. Our strategies enable the generation of
high quality training data without any human labeling. In addition, we propose
a heuristic reinforcement learning framework which substantially enhances
performance without requiring preference data. To further bridge the
performance gap with the larger server-side model, we propose an effective
approach that combines the mobile rewrite agent with the server model using a
cascade. To tailor the text rewriting tasks to mobile scenarios, we introduce
MessageRewriteEval, a benchmark that focuses on text rewriting for messages
through natural language instructions. Through empirical experiments, we
demonstrate that our on-device model surpasses the current state-of-the-art
LLMs in text rewriting while maintaining a significantly reduced model size.
Notably, we show that our proposed cascading approach improves model
performance.

使用指令调整的新方法构建了一个移动中心的文本改写模型，通过高质量的无需人工标注的训练数据和启发式强化学习框架提高性能，并通过级联方法将移动重写代理与服务器模型结合，从而在保持显著减小模型尺寸的同时，超越了目前最先进的大型语言模型在文本重写方面的性能。