The Role of Labels and Format in Model Learning

The model's ability to produce correct labels suggests that it focuses more on the format of the problem than on labels themselves./nSome studies suggest that these models may not be true metal learners, but rather formats that specify problems./nModels may engage in few-shot learning as they grow larger and require less data for fine-tuning./nThe ideal metal learning method would be able to use a small amount of data to solve new problems and improve the model simultaneously./nThe process of adapting to a new problem is typically separate from training the model itself, but it is desirable to have a model that can fine-tune on small amounts of data and improve with every task./nA potential future development is a lifelong online metal learning procedure where models can quickly adapt and improve with each new task.