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CLG: Contrastive Label Generation with Knowledge for Few-Shot Learning.
- Source :
- Mathematics (2227-7390); Feb2024, Vol. 12 Issue 3, p472, 21p
- Publication Year :
- 2024
-
Abstract
- Training large-scale models needs big data. However, the few-shot problem is difficult to resolve due to inadequate training data. It is valuable to use only a few training samples to perform the task, such as using big data for application scenarios due to cost and resource problems. So, to tackle this problem, we present a simple and efficient method, contrastive label generation with knowledge for few-shot learning (CLG). Specifically, we: (1) Propose contrastive label generation to align the label with data input and enhance feature representations; (2) Propose a label knowledge filter to avoid noise during injection of the explicit knowledge into the data and label; (3) Employ label logits mask to simplify the task; (4) Employ multi-task fusion loss to learn different perspectives from the training set. The experiments demonstrate that CLG achieves an accuracy of 59.237%, which is more than about 3% in comparison with the best baseline. It shows that CLG obtains better features and gives the model more information about the input sentences to improve the classification ability. [ABSTRACT FROM AUTHOR]
- Subjects :
- NATURAL language processing
BIG data
MOTOR learning
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Volume :
- 12
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Mathematics (2227-7390)
- Publication Type :
- Academic Journal
- Accession number :
- 175370093
- Full Text :
- https://doi.org/10.3390/math12030472