NCA-GENL Question 97
Select 3You are tasked with presenting the performance of a fine-tuned Large Language Model (LLM) on a text classification task. The dataset includes multiple accuracy metrics such as precision, recall, F1-score, and confusion matrix data. Which visualization techniques would be most effective for conveying these results clearly to a technical audience?
- A
A bar chart showing precision, recall, and F1-score for each class
- B
A scatter plot showing the distribution of accuracy across multiple datasets
- C
A heatmap to display the confusion matrix data
- D
A pie chart representing the percentage of correctly classified samples
- E
A line graph showing the trend of precision and recall over multiple epochs
Show answer and explanation
Correct answers: A, C, E
Explanation
Effective visualization of LLM performance requires selecting techniques that clearly represent the data being analyzed. Bar charts are useful for comparing class-level metrics, heatmaps are ideal for confusion matrix data, and line graphs are effective for showing trends over time, such as during training. Pie charts and scatter plots, while useful in other contexts, are less suited for detailed performance analysis in this scenario.
- A. Correct.
Bar charts are ideal for comparing metrics such as precision, recall, and F1-score across multiple classes, making it easier to understand performance differences.
- B. Incorrect.
Scatter plots are not typically used for summarizing classification metrics; they are better suited for visualizing relationships or distributions between two variables.
- C. Correct.
Heatmaps are excellent for visualizing confusion matrices, as they can clearly depict the distribution of true positive, true negative, false positive, and false negative values.
- D. Incorrect.
Pie charts are not suitable for representing performance metrics, as they fail to provide detailed insight into the relationship between different metrics or classes.
- E. Correct.
Line graphs are helpful for showing trends, such as how precision and recall evolve over multiple epochs during the training process.