Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning
<|reference_start|>Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning: While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Masked Language Models (MLMs) like BERT and RoBERTa achieve state-of-the-art results through fine-tuning but struggle with extending to few-shot and zero-shot settings due to their architectural constraints. Hence, we propose Statement-Tuning, a technique that models discriminative tasks as a set of finite statements and trains an Encoder model to discriminate between the potential statements to determine the label. We do Statement-Tuning on multiple tasks to enable cross-task generalization. Experimental results demonstrate that Statement Tuning achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. Moreover, the study investigates the impact of several design choices on few-shot and zero-shot generalization, revealing that Statement Tuning can achieve sufficient performance with modest training data and benefits from task and statement diversity for unseen task generalizability.<|reference_end|>
@article{elshabrawy2024enabling,
    title={Enabling Natural Zero-Shot Prompting on Encoder Models via
  Statement-Tuning},
    author={Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych, Alham Fikri Aji},
    journal={arXiv preprint arXiv:2404.12897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12897},
    primaryClass={cs.CL}
}