Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks to be performed through simple prompt-based interactions. Consequently, several approaches have been proposed to engineer prompts that most effectively enable LLMs to perform a given task (e.g., chain-of-thought prompting). In settings with a well-defined metric to optimize model performance, automatic prompt optimization (APO) methods have been developed to refine a seed prompt. Advancing this line of research, we propose APIO, a simple but effective prompt induction and optimization approach for the tasks of Grammatical Error Correction (GEC) and Text Simplification, without relying on manually specified seed prompts. APIO achieves a new state-of-the-art performance for purely LLM-based prompting methods on these tasks. We make our data, code, prompts, and outputs publicly available.
@article{arxiv.2508.09378,
title = {APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification},
author = {Artem Chernodub and Aman Saini and Yejin Huh and Vivek Kulkarni and Vipul Raheja},
journal= {arXiv preprint arXiv:2508.09378},
year = {2025}
}
Comments
Accepted for publication at Recent Advances in Natural Language Processing conference (RANLP 2025)