Towards an On-device Agent for Text Rewriting

Anonymous

TL;DR: We introduce a new approach for building a mobile-centric text rewriting model

Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting. However creating a smaller yet potent language model for text rewriting presents two formidable challenges: costly data collection and absence of emergent capabilities. In this paper we present solutions to address the above challenges. We propose an new instruction tuning method to develop a mobile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection. Moreover, to bridge the performance gap from the constraint size, we propose a cascading approach based on the confidence levels which are distilled from the large server model's critiques. To evaluate the text rewriting tasks for mobile scenarios, we introduce MessageRewriteEval, a human-labeled benchmark that focuses on text rewriting of 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 using public benchmark EditEval and our new benchmark. We show that our proposed cascading approach improves model performance further.

Paper Type: long

Research Area: NLP Applications

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources

Languages Studied: English