Effectively Prompting Small-sized Language Models for Cross-lingual Tasks via Winning Tickets
<|reference_start|>Effectively Prompting Small-sized Language Models for Cross-lingual Tasks via Winning Tickets: Current soft prompt methods yield limited performance when applied to small-sized models (fewer than a billion parameters). Deep prompt-tuning, which entails prepending parameters in each layer for enhanced efficacy, presents a solution for prompting small-sized models, albeit requiring carefully designed implementation. In this paper, we introduce the Lottery Ticket Prompt-learning (LTP) framework that integrates winning tickets with soft prompts. The LTP offers a simpler implementation and requires only a one-time execution. We demonstrate LTP on cross-lingual tasks, where prior works rely on external tools like human-designed multilingual templates and bilingual dictionaries, which may not be feasible in a low-resource regime. Specifically, we select a subset of parameters that have been changed the most during the fine-tuning with the Masked Language Modeling objective. Then, we prepend soft prompts to the original pre-trained language model and only update the selected parameters together with prompt-related parameters when adapting to the downstream tasks. We verify the effectiveness of our LTP framework on cross-lingual tasks, specifically targeting low-resource languages. Our approach outperforms the baselines by only updating 20\% of the original parameters.<|reference_end|>
@article{li2024effectively,
    title={Effectively Prompting Small-sized Language Models for Cross-lingual
  Tasks via Winning Tickets},
    author={Mingqi Li and Feng Luo},
    journal={arXiv preprint arXiv:2404.01242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01242},
    primaryClass={cs.CL}
}