NCA-GENL Question 108
Select 3You are tasked with fine-tuning a Large Language Model (LLM) for sentiment analysis using a labeled dataset. During the evaluation, the model shows inconsistent performance on customer reviews written in non-standard grammar. Which factors should you investigate to identify relationships or trends that might be affecting the results?
- A
The distribution of non-standard grammar examples in the training dataset
- B
The tokenization method used by the LLM for preprocessing the text
- C
The model's hyperparameter settings, such as learning rate and batch size
- D
The hardware architecture used to train the model
- E
The variability of sentiment labels within the dataset
Show answer and explanation
Correct answers: A, B, E
Explanation
To identify relationships or trends affecting the model's performance, it is crucial to examine the representation of non-standard grammar in the training dataset, the tokenization approach, and the quality of sentiment labels. These factors directly influence the model's ability to generalize to linguistic variations. Hyperparameters and hardware architecture are less likely to address this specific issue.
- A. Correct.
The distribution of non-standard grammar examples in the training dataset is critical because an imbalance could lead to the model underperforming on such examples.
- B. Correct.
The tokenization method directly affects how text is broken down for the model. If non-standard grammar is tokenized ineffectively, the model may struggle to understand it.
- C. Incorrect.
While hyperparameter settings are important for overall training, they are less likely to specifically explain the performance issue with non-standard grammar.
- D. Incorrect.
The hardware architecture impacts training efficiency but does not directly influence the model's ability to handle specific linguistic patterns like non-standard grammar.
- E. Correct.
The variability of sentiment labels, especially if inconsistently applied, can confuse the model and affect its ability to generalize well to non-standard grammar.