Generative AI with LLMs: Multi-Task Finetuning and FLAN-T5: What You Need to Know

Learn what multi-task finetuning is, how it can prevent catastrophic forgetting, and how FLAN-T5 uses it to achieve impressive results on various natural language tasks.

Question

Which of the following statements about multi-task finetuning is correct? Select all that apply:

A. Multi-task finetuning can help prevent catastrophic forgetting.
B. Performing multi-task finetuning may lead to slower inference.
C. Multi-task finetuning requires separate models for each task being performed.
D. FLAN-T5 was trained with multi-task finetuning.

Answer

A. Multi-task finetuning can help prevent catastrophic forgetting.
D. FLAN-T5 was trained with multi-task finetuning.

Explanation

The correct answers are A and D. Multi-task finetuning can help prevent catastrophic forgetting and FLAN-T5 was trained with multi-task finetuning.

Catastrophic forgetting is the phenomenon where a neural network forgets previously learned information when learning new information. This can happen when fine-tuning a model on a single task, as the model may overwrite the weights that are important for other tasks. Multi-task finetuning is a technique that allows the model to learn from multiple tasks simultaneously, by optimizing a shared objective function that combines the losses from each task. This can help the model to retain the knowledge from different tasks and improve its generalization ability.

FLAN-T5 is a language model that has been fine-tuned on a mixture of tasks, such as recipe generation, recipe translation, and recipe description. FLAN-T5 is based on the T5 model, which is a text-to-text transformer that can perform any natural language task by converting it into a text generation problem. FLAN-T5 uses a technique called instruction fine-tuning, which trains the model on examples of instructions and how the model should respond to those instructions. For example, the instruction could be “Write a summary of the following article” and the model should produce a summary as the output. Instruction fine-tuning enables the model to generalize to new tasks that are specified by natural language instructions at inference time.

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