AIF-C01 Question 146
Select 3You are tasked with fine-tuning a foundation model for a customer service chatbot. The training dataset contains thousands of chat transcripts, but the data is unstructured and contains inconsistent formats, redundant entries, and some irrelevant conversations. Which steps should you take to prepare the data before fine-tuning the model?
- A
Clean and normalize the data to remove inconsistencies and irrelevant entries.
- B
Ensure the dataset is representative of the scenarios the chatbot will handle in production.
- C
Label the data with the expected outputs, such as intent categories or response types.
- D
Increase the size of the dataset by duplicating existing entries to achieve larger training data.
- E
Incorporate reinforcement learning from human feedback (RLHF) during data preparation.
Show answer and explanation
Correct answers: A, B, C
Explanation
Preparing data for fine-tuning a foundation model involves ensuring data quality, representativeness, and proper labeling. Cleaning and normalizing the data removes inconsistencies, while representativeness ensures the model generalizes well to real-world scenarios. Labeling the data provides the supervised signals needed during the fine-tuning process. RLHF, while useful during optimization, is not part of the initial data preparation phase, and duplicating data can harm model training by introducing biases.
- A. Correct.
Cleaning and normalizing data is essential to remove inconsistencies and irrelevant information, ensuring high-quality input for fine-tuning.
- B. Correct.
A representative dataset ensures that the model learns from diverse scenarios and performs well in real-world use cases.
- C. Correct.
Labeling the data with expected outputs, such as intents or categories, is crucial for supervised fine-tuning tasks.
- D. Incorrect.
Duplicating existing entries does not increase the diversity or quality of the data and can lead to overfitting during training.
- E. Incorrect.
While reinforcement learning from human feedback (RLHF) is a valuable technique for optimizing model behavior, it is not a direct step in preparing the dataset for fine-tuning.