AIF-C01 Question 142
Single answerA company wants to use a pre-trained foundation model to power a customer support chatbot that understands domain-specific terminology for the healthcare industry. What is the most appropriate method to customize the model for this use case?
- A
Instruction tuning to teach the model how to perform specific tasks using labeled examples
- B
Adapting the model for the healthcare domain using transfer learning with domain-specific data
- C
Continuous pre-training on a general-purpose dataset to improve the model's overall knowledge
- D
Re-training the entire foundation model from scratch using only healthcare-specific data
Show answer and explanation
Correct answer: B
Explanation
For customizing a pre-trained foundation model for a specific domain like healthcare, transfer learning is the most appropriate method. Transfer learning allows you to leverage the knowledge the model has already learned and refine it using domain-specific data. This approach is efficient and practical compared to alternatives like re-training from scratch.
- A. Incorrect.
Instruction tuning is used to teach the model how to perform specific tasks but does not involve adapting the model to a specific domain like healthcare.
- B. Correct.
Adapting the model using transfer learning with domain-specific data is the most appropriate approach. This method fine-tunes the pre-trained model using data from the healthcare domain, enabling it to understand and generate content specific to the industry.
- C. Incorrect.
Continuous pre-training on general-purpose datasets helps improve the model's general knowledge but does not tailor it to a specific domain such as healthcare.
- D. Incorrect.
Re-training the entire foundation model from scratch is not feasible due to the high computational cost and large data requirements. It is also unnecessary since the pre-trained model can be fine-tuned for this specific use case.