Google AI recently introduced their new Natural Language Processing (NLP) model, known as Fine-tuned LAnguage Net (FLAN), which explores a simple technique called instruction fine-tuning, or instruction tuning for short. In general, fine-tuning requires a large number of training examples, along with stored model weights for each downstream task which is not always practical, particularly for large models. FLAN's instruction fine-tuning technique involves fine-tuning a model not to solve a specific task, but to also make it more amenable to solving NLP tasks in particular. FLAN is fine-tuned on a large set of varied instructions that use a simple and intuitive description of the task, such as "Classify this movie review as positive or negative," or "Translate this sentence to Danish." Creating a dataset of instructions from scratch to fine-tune the model would take a considerable amount of resources.