NCA-GENL Question 111
Select 4An AI team is evaluating a generative language model's performance after fine-tuning it with Reinforcement Learning from Human Feedback (RLHF). They conduct an experiment to assess the quality of generated responses using human evaluators. Which best practices should they follow in this scenario to ensure reliable and unbiased results?
- A
Provide clear and consistent evaluation criteria to human evaluators.
- B
Randomize the order of model-generated responses during evaluation.
- C
Use only positive feedback to guide the reinforcement learning process.
- D
Instruct human evaluators to focus solely on grammatical correctness of responses.
- E
Ensure diversity in the demographic background of human evaluators.
- F
Conduct multiple rounds of evaluation to validate consistency of results.
Show answer and explanation
Correct answers: A, B, E, F
Explanation
To ensure reliable and unbiased evaluation of a generative model fine-tuned with RLHF, it is critical to follow best practices such as providing clear criteria, randomizing response order, ensuring evaluator diversity, and validating results across multiple rounds. These practices collectively reduce biases, enhance fairness, and improve the reliability of the evaluation process.
- A. Correct.
Providing clear and consistent evaluation criteria helps human evaluators make objective judgments, reducing ambiguity and bias in the evaluation process.
- B. Correct.
Randomizing the order of model-generated responses minimizes positional bias, ensuring that evaluators assess outputs fairly without being influenced by their sequence.
- C. Incorrect.
Using only positive feedback is not a best practice because it limits the model's ability to learn from errors, which is critical in RLHF for improving performance.
- D. Incorrect.
Focusing solely on grammatical correctness ignores other important aspects like relevance, coherence, and ethical considerations in the generated responses.
- E. Correct.
Ensuring diversity in the demographic background of human evaluators helps reduce cultural or regional biases in the evaluation process, making the assessments more robust and inclusive.
- F. Correct.
Conducting multiple rounds of evaluation allows the team to validate the consistency of results, ensuring that the model's performance is not evaluated based on a single, potentially biased set of assessments.