mlsanityData to 7 Oct 2026

Zero-Label Prompt Selection

arXiv 2211.04668 ↗First posted 9 Nov 2022cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 36 of its 51 references that are in the map. First posted on arXiv 9 Nov 2022.

Builds on

36 of 51 references.

References · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  4. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  7. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  8. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  9. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  10. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  11. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  12. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
24 more
  1. 2019How Can We Know What Language Models Know?cited by 953
  2. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  3. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 798
  4. 2021GPT Understands, Toocited by 715
  5. 2021ClipCap: CLIP Prefix for Image Captioningcited by 557
  6. 2018WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representationscited by 370
  7. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
  8. 2020Rethinking Pre-training and Self-trainingcited by 352
  9. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  10. 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 288
  11. 2019Label Propagation for Deep Semi-supervised Learningcited by 287
  12. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  13. 2021PPT: Pre-trained Prompt Tuning for Few-shot Learningcited by 232
  14. 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
  15. 2019Revisiting Self-Training for Neural Sequence Generationcited by 154
  16. 2022GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Modelscited by 127
  17. 2021Towards Zero-Label Language Learningcited by 76
  18. 2022Controllable Natural Language Generation with Contrastive Prefixescited by 75
  19. 2022Prompt Consistency for Zero-Shot Task Generalizationcited by 66
  20. 2022ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalizationcited by 54
  21. 2021Reordering Examples Helps during Priming-based Few-Shot Learningcited by 50
  22. 2022Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Modelscited by 34
  23. 2022Co-training Improves Prompt-based Learning for Large Language Modelscited by 24
  24. 2021FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understandingcited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2024Smoothie: Label Free Language Model Routingcited by 31
  2. 2023Improving Probability-based Prompt Selection Through Unified Evaluation and Analysiscited by 13
  3. 2023Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Modelscited by 6
  4. 2026Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selectioncited by 0