Analyzing Bias in Synthetic Dataset Generation
Based on the scenario, analyze the primary issue with this synthetically generated dataset and explain the most likely consequence this will have on the final trained sentiment analysis model's performance.
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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
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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