Few-shot learning, particularly in natural language processing, shows promise through the Noisy Channel Language Model Prompting, enhancing classification amidst imbalanced data and unseen labels.
Traditional few-shot learning models struggle with imbalanced data as they tend to favor categories with abundant training examples, leading to poor performance for underrepresented labels.
Inspired by machine translation, Noisy Channel Language Model Prompting revamps probabilistic calculations, lending robustness in predicting categories with limited examples, crucial for domains like medical text classification.
When faced with imbalanced datasets, traditional methods might misclassify phrases due to insufficient examples of certain categories; however, the Noisy Channel approach addresses this issue effectively.
#few-shot-learning #natural-language-processing #noisy-channel-model #imbalanced-data #text-classification
[
Collection
]
[
|
...
]