#few-shot-learning

#few-shot-learning

The study focuses on testing a model's out-of-domain generalization through generated images of imaginary objects.

LLMs' accuracy is influenced by the number of distractors in prompts, with some models faring better than others in different reasoning scenarios.

Noisy Channel Language Model Prompting improves few-shot learning by addressing imbalanced data challenges and enhancing model predictions with limited examples.

The study focuses on testing a model's out-of-domain generalization through generated images of imaginary objects.

LLMs' accuracy is influenced by the number of distractors in prompts, with some models faring better than others in different reasoning scenarios.

Noisy Channel Language Model Prompting improves few-shot learning by addressing imbalanced data challenges and enhancing model predictions with limited examples.