AWS Certified AI Practitioner Practice Exam

Which data source should a social media company use for bias evaluation in LLM outputs?

1. User-generated content

2. Moderation logs

3. Content moderation guidelines

4. Benchmark datasets

The correct answer is: Benchmark datasets

Choosing benchmark datasets as the data source for bias evaluation in large language model (LLM) outputs is appropriate for several reasons. Benchmark datasets are specifically curated to assess the performance of machine learning models against defined metrics, including bias and fairness. They typically include a diverse array of examples designed to test the model across various demographic groups and scenarios. Using benchmark datasets allows for a standardized way to measure and compare the performance of the LLM, providing a clear understanding of any biases that may exist in the outputs. These datasets often contain known labels and classifications that can highlight instances of bias, making it easier to measure the fairness of the model's predictions. Furthermore, the structured nature of benchmarks facilitates reproducibility of the evaluation process, which is critical in ensuring that any findings related to bias can be validated independently. In contrast, user-generated content may contain a wide range of views and expressions but lacks the systematic approach needed for evaluating bias effectively. Moderation logs are helpful for understanding user interactions but may not specifically capture bias across diverse groups. Content moderation guidelines provide rules for managing content but do not serve as a testing ground for evaluating the model's output biases.