Google’s FLAN dataset shows how fine-tuning helps LLMs learn multiple tasks at once. It trains models to handletranslation, summarization, and question-answering within a single system. Instead of specializing in one area, FLAN helps models generalize across different tasks. Annotators created a ...
In-context learning is a prompting technique used with LLMs, particularly GPT-3, to facilitate few-shot learning. The idea is to provide examples of the desired input-output pairs within the prompt, allowing the model to generalize from these examples and produce the correct output for a given...