Zero-Label Prompt Selection

Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance is highly sensitive to the choice of prompts, while selecting a high-performing prompt is challenging given the scarcity of labels. To address the issue, we propose a Zero-Label Prompt Selection (ZPS) method that selects prompts without any labeled data or gradient update. Specifically, given the candidate human-written prompts for a task, ZPS labels a set of unlabeled data with a prompt ensemble and uses the pseudo-labels for prompt selection. Experiments show that ZPS improves over prior methods by a sizeable margin in zero-label performance. We also extend ZPS to a few-shot setting and show its advantages over strong baselines such as prompt tuning and model tuning. Recently, extensive studies have shown that large language models (LLMs) have promising performance for few-shot learning (Brown et al., 2020; Zhao et al., 2021; Schick & Schütze, 2021; Gao et al., 2021), and they even show strong generalization abilities to new tasks without any annotated data (Brown et al., 2020; Wei et al., 2021; Sanh et al., 2021). Different from conventional fine-tuning methods that require expensive parameter updates for each downstream task, prompts are employed to provide in-context information or task instructions, which is helpful for guiding models to perform each task. Manually-written prompts are often used to specify the task and unify the format of inputs. However, the performance of different prompts during evaluation can vary from near state-of-the-art to random guess; e.g., using a non-optimal prompt can cause a performance drop of up to 60 points on the CB task (Zhao et al., 2021).