AIF-C01 Question 139
Single answerYour company is building a chatbot using a foundation model to handle customer support inquiries. The team has pre-trained the model on a massive general text dataset. However, the chatbot struggles to answer domain-specific questions related to your company's products. What is the next step the team should take to improve the model's performance?
- A
Perform fine-tuning using a labeled dataset specific to your company's products.
- B
Continue pre-training using the same general dataset to further improve the model.
- C
Deploy the model as-is and rely on user feedback for self-improvement.
- D
Use transfer learning to train a new model from scratch on your company's dataset.
Show answer and explanation
Correct answer: A
Explanation
When a foundation model struggles with domain-specific tasks, fine-tuning is the most appropriate method to improve performance. It allows the model to adapt to a specific domain by training it on a smaller labeled dataset relevant to the application, making it more effective for tasks like answering product-related inquiries.
- A. Correct.
Fine-tuning involves training the pre-trained model on a smaller, domain-specific dataset to specialize the model for a particular task or domain, making it the most appropriate step in this scenario.
- B. Incorrect.
Continuing pre-training on the same general dataset would not address the issue because the model already lacks domain-specific knowledge, and further pre-training would not add this specialization.
- C. Incorrect.
Deploying the model as-is would not improve its ability to handle domain-specific questions, as the current shortcomings in understanding your company's products would remain unaddressed.
- D. Incorrect.
Using transfer learning to train a new model from scratch is unnecessary and inefficient because the foundation model is already pre-trained, and fine-tuning can efficiently specialize it for your needs.