@inproceedings{nag-etal-2025-mutantprompt,
    title = "{M}utant{P}rompt: Prompt Optimization via Mutation Under a Budget on Modest-sized {LM}s",
    author = "Nag, Arijit  and
      Mukherjee, Animesh  and
      Ganguly, Niloy  and
      Chakrabarti, Soumen",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.1139/",
    pages = "22082--22092",
    ISBN = "979-8-89176-256-5",
    abstract = "Prompts serve as a critical instruction interface to unlock the diverse capabilities of Large Language Models (LLMs), thus directly influencing the quality of their outputs. While prompt engineering has shown great promise, identifying optimal prompts remains a significant challenge, particularly for low-resource languages, which often face higher computational costs due to increased token generation and limited gold standard task data. In response, we propose MutantPrompt, a framework that leverages multi-armed bandit algorithms to efficiently identify optimal prompts tailored to low-resource languages. By framing prompt selection as an exploration-exploitation problem under a fixed computational budget, the framework dynamically balances exploring new prompts with exploiting known high-performing ones. We demonstrate the framework{'}s effectiveness across multiple low-resource Indic language tasks, including classification, question-answering and causal reasoning using three small parameter-size LLMs. The results highlight the cost efficiency of the search method in finding optimal prompts and resulting performance improvements."
}