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.
Code (2)
Similar Papers 제목 키워드 기반
What Makes Pre-trained Language Models Better Zero-shot Learners?
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a r…
Language ModellingPrompt Learningtext-classificationText Classification+1Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data
Anomaly detection is the task of identifying abnormal samples in large unlabeled datasets. While the advent of foundation models has produced powerful zero-shot anomaly detection methods, their deployment in practice is …
Anomaly DetectionModel SelectionUnsupervised Anomaly Detectionzero-shot anomaly detectionZero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled examples of anomalies. However, existing ZS…
Test-time AdaptationAnomaly DetectionBoosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection
Large language models (LLMs) are very proficient text generators. We leverage this capability of LLMs to generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target lang…
Cross-Lingual TransferNatural Language InferenceSentiment AnalysisUNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain. We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joi…
Dialogue State TrackingTransfer Learning