@inproceedings{chernodub-etal-2025-apio,
title = "{APIO}: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification",
author = "Chernodub, Artem and
Saini, Aman and
Huh, Yejin and
Kulkarni, Vivek and
Raheja, Vipul",
editor = "Angelova, Galia and
Kunilovskaya, Maria and
Escribe, Marie and
Mitkov, Ruslan",
booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ranlp-1.28/",
pages = "234--239",
abstract = "Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks 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. Subsequently, 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."
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<abstract>Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks 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. Subsequently, 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.</abstract>
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%0 Conference Proceedings
%T APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
%A Chernodub, Artem
%A Saini, Aman
%A Huh, Yejin
%A Kulkarni, Vivek
%A Raheja, Vipul
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F chernodub-etal-2025-apio
%X Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks 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. Subsequently, 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.
%U https://aclanthology.org/2025.ranlp-1.28/
%P 234-239
Markdown (Informal)
[APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification](https://aclanthology.org/2025.ranlp-1.28/) (Chernodub et al., RANLP 2025)
ACL