NCA-GENL Question 109
Select 3A team is evaluating a generative AI model by using Reinforcement Learning from Human Feedback (RLHF). During the process, they observe that some human labelers provide inconsistent feedback, while others display potential bias in their preferences. What steps should the team take to ensure reliable evaluation and training of the model?
- A
Provide labelers with detailed guidelines and training to reduce inconsistencies.
- B
Incorporate a diverse group of labelers to minimize bias in the feedback.
- C
Ignore inconsistent feedback from labelers to speed up the evaluation process.
- D
Use majority voting or aggregation techniques to determine consensus among labelers.
- E
Automatically assume that feedback from experienced labelers is always correct.
Show answer and explanation
Correct answers: A, B, D
Explanation
When using RLHF for model evaluation, it is essential to ensure that feedback from human labelers is consistent, unbiased, and reliable. This can be achieved by providing proper training to labelers, incorporating diverse perspectives to reduce bias, and using aggregation methods like majority voting to handle inconsistencies. Ignoring inconsistent feedback or overly relying on experienced labelers without verification can lead to unreliable evaluation and suboptimal model performance.
- A. Correct.
Providing labelers with clear guidelines and training helps ensure consistency in their feedback, reducing variability caused by misunderstanding or lack of expertise.
- B. Correct.
Incorporating a diverse group of labelers helps reduce systemic biases, as it ensures feedback comes from individuals with different perspectives and backgrounds.
- C. Incorrect.
Ignoring inconsistent feedback outright can lead to valuable data being discarded unnecessarily. Instead, inconsistencies should be analyzed and addressed systematically.
- D. Correct.
Using majority voting or aggregation techniques allows for a more robust and objective evaluation by identifying the most common feedback among labelers, which can help mitigate individual biases or errors.
- E. Incorrect.
Assuming that experienced labelers are always correct is a flawed approach, as even experienced labelers can make mistakes or have biases that impact their feedback.