APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification

TL;DR

APIO introduces an automatic prompt induction and optimization method that enhances large language model performance on grammatical error correction and text simplification without manual prompts, achieving state-of-the-art results.

Contribution

It presents a novel, seed-prompt-free approach for prompt induction and optimization, advancing automatic prompt engineering for NLP tasks.

Findings

Achieves state-of-the-art performance on GEC and text simplification tasks.

Does not rely on manually specified seed prompts.

Provides publicly available data, code, and prompts.

Abstract

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.