A team is using a large language model to generate a synthetic dataset for training a sentiment classifier. The goal is to classify user feedback into 'Positive', 'Negative', or 'Neutral' categories. After generating 10,000 examples using a general prompt to create feedback, they find that approximately 80% of the generated samples are 'Positive', 15% are 'Neutral', and only 5% are 'Negative'. Which statement best analyzes the primary issue with this generated dataset and its most likely consequence for the classifier?
0
1
Tags
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Input Inversion for Mitigating Data Generation Bias
Analyzing Bias in Synthetic Dataset Generation
A team is using a large language model to generate a synthetic dataset for training a sentiment classifier. The goal is to classify user feedback into 'Positive', 'Negative', or 'Neutral' categories. After generating 10,000 examples using a general prompt to create feedback, they find that approximately 80% of the generated samples are 'Positive', 15% are 'Neutral', and only 5% are 'Negative'. Which statement best analyzes the primary issue with this generated dataset and its most likely consequence for the classifier?
Critiquing a Synthetic Data Generation Method