AIF-C01 Question 148
Select 2A company is using a foundation model for text generation and wants to evaluate its performance. Which of the following methods are appropriate for evaluating the foundation model's performance?
- A
Use BLEU (Bilingual Evaluation Understudy) score to measure the quality of generated text against a reference text.
- B
Calculate the model's accuracy based on the number of correctly predicted tokens in the generated text.
- C
Conduct human evaluations to assess the relevance and fluency of the generated text.
- D
Use confusion matrices to analyze how well the foundation model distinguishes between different text categories.
- E
Apply F1-score to evaluate the balance between precision and recall for text classification tasks.
Show answer and explanation
Correct answers: A, C
Explanation
Foundation model performance for text generation is best evaluated using task-specific methods. BLEU is a quantitative metric that compares generated text with reference texts, while human evaluations provide qualitative insights into aspects like fluency and relevance. Metrics like accuracy, confusion matrices, and F1-score are better suited for classification or prediction tasks rather than text generation.
- A. Correct.
BLEU score is a widely used metric for evaluating text generation models. It compares the generated text with reference texts to assess quality.
- B. Incorrect.
Accuracy is not commonly used for evaluating text generation tasks, as it does not effectively capture nuances like fluency or relevance in generated text.
- C. Correct.
Human evaluations are a valid and commonly used approach to assess the quality of generated text, particularly for subjective measures like fluency and relevance.
- D. Incorrect.
Confusion matrices are more suited for classification tasks rather than evaluating the quality of text generation models.
- E. Incorrect.
The F1-score is relevant for classification tasks but is not applicable for evaluating the performance of text generation models.