Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning

• URL: http://arxiv.org/abs/2404.12897v3
• Date: Thu, 17 Oct 2024 05:21:24 GMT
• Title: Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning
• Authors: Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych, Alham Fikri Aji,
• Abstract summary: Statement-Tuning is 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. Experimental results demonstrate that Statement-Tuning achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. The study investigates the impact of several design choices on few-shot and zero-shot generalization, revealing that Statement-Tuning can achieve strong performance with modest training data.
• Score: 55.265138447400744
• License:
• Abstract: 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 strong performance with modest training data and benefits from task and statement diversity for unseen task generalizability.

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