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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.


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.


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


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.

Generative AI Exam Question and Answer

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Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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