mlsanityData to 7 Oct 2026

Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

arXiv 2204.06305 ↗First posted 13 Apr 2022cs.CL cs.AI cs.LGcited by 8

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Abstract (arXiv)

Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting. Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template. Our experiments demonstrate that AMuLaP achieves competitive performance on the GLUE benchmark without human effort or external resources.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 13 Apr 2022.

Builds on

22 of 32 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  5. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  7. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  8. 2019Language Models as Knowledge Bases?cited by 1,922
  9. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  10. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  11. 2019How Can We Know What Language Models Know?cited by 953
  12. 2018Neural Network Acceptability Judgmentscited by 952
10 more
  1. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  2. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  3. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 376
  4. 2021True Few-Shot Learning with Language Modelscited by 343
  5. 2021Factual Probing Is [MASK]: Learning vs. Learning to Recallcited by 291
  6. 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 288
  7. 2021How Many Data Points is a Prompt Worth?cited by 197
  8. 2019Commonsense Knowledge Mining from Pretrained Modelscited by 189
  9. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  10. 2020Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classificationcited by 98

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 0; 2025: 0; 2026: 1.

8 papers.

  1. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  2. 2022Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Codingcited by 40
  3. 2023DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognitioncited by 23
  4. 2022Pre-trained Language Models Can be Fully Zero-Shot Learnerscited by 22
  5. 2023Language models are weak learnerscited by 21
  6. 2023Automated Few-shot Classification with Instruction-Finetuned Language Modelscited by 9
  7. 2023Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Modelscited by 7
  8. 2026Making Grid Beam Search Less Greedycited by 0