NCA-GENL Question 112
Select 3A data scientist is tasked with fine-tuning a generative AI model using reinforcement learning from human feedback (RLHF). During the evaluation phase, the scientist plans to use human evaluators to rank the model's outputs for quality and relevance. What are the primary considerations the scientist should take into account to ensure valid and reliable results from this experiment?
- A
Ensure a diverse set of human evaluators to minimize bias in rankings.
- B
Use automated metrics exclusively, as human feedback is inherently subjective and unreliable.
- C
Clearly define evaluation criteria for the human evaluators to ensure consistency.
- D
Compensate human evaluators based on the speed of their feedback to maximize efficiency.
- E
Incorporate statistical methods to analyze inter-annotator agreement for reliability.
Show answer and explanation
Correct answers: A, C, E
Explanation
RLHF experiments involving human evaluators require careful planning to ensure valid and reliable results. Diversity in evaluators minimizes bias, clear evaluation criteria establish consistency, and statistical methods like inter-annotator agreement help assess reliability. While automated metrics and efficient compensation are considerations, they cannot replace or undermine the quality and reliability of human feedback, which is central to RLHF.
- A. Correct.
Including a diverse set of human evaluators helps minimize potential biases that could arise from homogeneous perspectives, making the feedback more representative.
- B. Incorrect.
Automated metrics are useful but cannot capture nuanced human preferences as effectively as direct human feedback, which is critical in RLHF experiments.
- C. Correct.
Clearly defining evaluation criteria ensures that human evaluators have a consistent understanding of the task, reducing variability in their assessments.
- D. Incorrect.
Compensating human evaluators based solely on speed risks incentivizing rushed and potentially low-quality feedback, which undermines the reliability of the experiment.
- E. Correct.
Statistical analysis of inter-annotator agreement (e.g., Cohen's kappa) is important to measure how consistently evaluators are applying the criteria, ensuring the reliability of the results.