APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
This work addresses the challenge of prompt engineering for NLP practitioners by automating prompt induction and optimization, though it is incremental as it builds on existing automatic prompt optimization methods.
The paper tackles the problem of automatically generating and optimizing prompts for large language models in grammatical error correction and text simplification, achieving state-of-the-art performance for purely LLM-based prompting methods on these tasks.
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.