APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909735636172800 |
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| author | Chernodub, Artem Saini, Aman Huh, Yejin Kulkarni, Vivek Raheja, Vipul |
| author_facet | Chernodub, Artem Saini, Aman Huh, Yejin Kulkarni, Vivek Raheja, Vipul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification Chernodub, Artem Saini, Aman Huh, Yejin Kulkarni, Vivek Raheja, Vipul Computation and Language Artificial Intelligence I.2.7 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. |
| title | APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification |
| topic | Computation and Language Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2508.09378 |