AIF-C01 Question 144
Select 3You are tasked with fine-tuning a foundation model using a dataset for a customer support chatbot. Which actions should you take to ensure the dataset is properly prepared for fine-tuning the model?
- A
Ensure the dataset includes a diverse range of customer queries representative of real-world use cases.
- B
Label the dataset with specific intents and responses for supervised learning.
- C
Use only a small dataset with minimal diversity to reduce training time.
- D
Incorporate reinforcement learning from human feedback (RLHF) to align the model's responses with human preferences.
- E
Include personally identifiable information (PII) in the dataset to improve personalization.
Show answer and explanation
Correct answers: A, B, D
Explanation
To fine-tune a foundation model effectively, it is crucial to prepare the dataset by making it diverse and representative of real-world scenarios, labeling it for supervised learning, and optionally using RLHF to improve alignment with human preferences. Avoid practices like including PII or using insufficiently diverse datasets, as these can lead to ethical and performance issues.
- A. Correct.
Ensuring the dataset includes diverse and representative customer queries helps the model generalize effectively to real-world scenarios, which is critical for fine-tuning.
- B. Correct.
Labeling the dataset with specific intents and responses is necessary for supervised learning, enabling the model to learn the desired behavior.
- C. Incorrect.
Using a small and minimally diverse dataset may reduce training time, but it can lead to a poorly performing model that cannot generalize to real-world inputs.
- D. Correct.
Incorporating reinforcement learning from human feedback (RLHF) aligns the model with human preferences, ensuring more accurate and appropriate responses during fine-tuning.
- E. Incorrect.
Including personally identifiable information (PII) in the dataset violates data governance and security best practices. It should be avoided to ensure ethical AI development.