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

2025-08-12

AI Summary

This work addresses grammatical error correction (GEC) and text simplification (TS), proposing APIO—an automatic prompt induction and optimization framework that eliminates reliance on manually crafted seed prompts. APIO establishes an end-to-end, LLM-based prompt optimization pipeline, iteratively generating and selecting prompt templates guided by quantifiable evaluation metrics (e.g., GLEU, SARI). Crucially, it introduces the first fully seed-free, purely automated prompt optimization method—requiring no human-provided initial prompts. On standard benchmarks—including CoNLL-2014, BEA-2019, and TurkCorpus—APIO achieves state-of-the-art performance among prompt-only approaches and significantly outperforms existing automated prompt optimization techniques (e.g., PromptBreeder, AutoPrompt). By enabling effective LLM adaptation with minimal human intervention and low resource overhead, APIO establishes a novel paradigm for efficient, low-resource LLM customization.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Automatically induce and optimize prompts for GEC
• Improve text simplification without manual prompts
• Enhance LLM performance in NLP tasks automatically

Innovation

• Automatic prompt induction without manual seeds
• Optimization for grammatical error correction
• State-of-the-art LLM-based prompting performance