Few-shot learning is a model adaptation resulting from few-shot prompting, in which the model changes from being unable to solve the task to being able to solve it thanks to the provided examples. In the context of LLMs, the “learning” is temporary and only applies to a particular chat conversation.
Jul 10, 2024
6 hours ago · Few-shot learning is an ML technique designed to expand a model's existing training using limited new examples or data points. It's useful for adding new data ...
Jul 11, 2024 · Few-shot learning enables us to train models effectively even when labeled data is limited or expensive to acquire. This opens up new possibilities in domains ...
Jul 6, 2024 · Few-Shot Learning (FSL) involves training models with a very small amount of labeled data. The goal is to achieve high performance with minimal supervision, ...
Jul 24, 2024 · Few-shot learning (FSL) takes a slightly different approach. This enables a model to learn new tasks with only a few examples. This technique is particularly ...
Jul 18, 2024 · We introduce EVOLVEpro, a few-shot active learning framework to rapidly improve protein activity using a combination of PLMs and protein activity predictors, ...
Jul 23, 2024 · Few-shot prompting is a technique that involves providing a language model with a small number of examples to guide its response to a specific task. This method ...
Jul 2, 2024 · Our approach, named FSFP (Few-Shot Learning for Protein Fitness Prediction), is notable for its reliance on a minimal labeled dataset for the target protein, ...
Jul 10, 2024 · Among the most exciting advancements are zero-shot and few-shot learning, which enable these models to perform tasks with little to no specific training data.
Jul 6, 2024 · Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338. ↩. Few-shot prompting is also called in-context learning.