AIF-C01 Question 150
Select 3A data science team is evaluating the performance of a foundation model trained for natural language understanding tasks. They want to ensure the model is robust across multiple use cases, including sentiment analysis, text summarization, and machine translation. Which approaches should they use to evaluate the foundation model's performance effectively?
- A
Use benchmark datasets specifically designed for each task, such as GLUE for natural language understanding.
- B
Conduct human evaluation by asking domain experts to rate the output quality for specific use cases.
- C
Evaluate the model solely based on its training data performance metrics, such as accuracy and loss.
- D
Use synthetic datasets generated by the foundation model itself for performance evaluation.
- E
Perform cross-task evaluation by testing the model on tasks for which it was not explicitly trained.
Show answer and explanation
Correct answers: A, B, E
Explanation
Evaluating foundation model performance requires a combination of quantitative and qualitative approaches. Benchmark datasets provide standardized metrics, human evaluation ensures quality in subjective tasks, and cross-task evaluation assesses model generalization. Relying solely on training data or synthetic datasets compromises the reliability of the evaluation.
- A. Correct.
Benchmark datasets are specifically designed for evaluating model performance on standardized tasks, making this a reliable approach.
- B. Correct.
Human evaluation provides qualitative insights into the model's output quality, especially for subjective tasks like sentiment analysis.
- C. Incorrect.
Evaluating solely on training data performance can lead to overfitting issues and does not reflect real-world performance.
- D. Incorrect.
Synthetic datasets generated by the model itself could introduce bias and do not provide an objective evaluation of its capabilities.
- E. Correct.
Cross-task evaluation tests the adaptability and generalization of the foundation model, which is crucial for foundation models designed to handle diverse tasks.