@inproceedings{zhao-etal-2025-simple,
    title = "A Simple-Yet-Efficient Instruction Augmentation Method for Zero-Shot Sentiment Classification",
    author = "Zhao, Yang  and
      Muraoka, Masayasu  and
      Yoshida, Issei  and
      Bhattacharjee, Bishwaranjan  and
      Kanayama, Hiroshi",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.107/",
    pages = "1585--1599",
    abstract = "Instruction tuning significantly enhances the performance of large language models in tasks such as sentiment classification. Previous studies have leveraged labeled instances from sentiment benchmark datasets to instruction-tune LLMs, improving zero-shot sentiment classification performance. In this work, we propose a simple-yet-efficient instruction augmentation method which does not rely on any actual labeled sentiment instances. With just 240 pseudo instruction instances, the proposed method significantly improve the classification performance across several LLMs on 12 benchmark datasets, increasing scores by 30 points and outperforming LLMs that utilize more complex instruction tuning methods by 5.1 points. Surprisingly, the models tuned with 240 pseudo-instructions even outperform those tuned with actual domain-specific instruction instances. Despite method`s simplicity, our further analysis suggests that the probability shift toward the positive and negative classes and its generalization ability may be the primary driver of the improvement."
}