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

2025/08/12 by Chernodub, Artem, Saini, Aman, Huh, Yejin +2
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7

paper · doi:10.48550/arxiv.2508.09378

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.

Citations

• A Survey of Automatic Prompt Engineering: An Optimization Perspective
• Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification
• GPT-3.5 for Grammatical Error Correction
• Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
• Are Large Language Models Good Prompt Optimizers?
• BLESS: Benchmarking Large Language Models on Sentence Simplification
• Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
• Metric-Based In-context Learning: A Case Study in Text Simplification
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli
• Lost in the Middle: How Language Models Use Long Contexts
• Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
• CoEdIT: Text Editing by Task-Specific Instruction Tuning
• Automatic Prompt Optimization with "Gradient Descent" and Beam Search
• Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
• Large Language Models Are Human-Level Prompt Engineers
• RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
• Instruction Induction: From Few Examples to Natural Language Task Descriptions
• ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification\n Models with Multiple Rewriting Transformations
• EASSE: Easier Automatic Sentence Simplification Evaluation
• EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
• Prefix-Tuning: Optimizing Continuous Prompts for Generation
• Prefix-Tuning: Optimizing Continuous Prompts for Generation

Related

• A Systematic Survey of Automatic Prompt Optimization Techniques
• Autonomous Prompt Engineering in Large Language Models
• Large Language Models Are Human-Level Prompt Engineers
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
• Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models