1. Methods for high-quality feedback gathering
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2. Methods for high-quality feedback gathering
Gathering clear, accurate feedback is key to the RLHF process.
3. Methods for high-quality feedback gathering
In fact, it is essential for training reward models that reflect human preferences.
Two main methods for feedback collection are pairwise comparisons and ratings.
4. Pairwise comparisons
Pairwise comparisons involve asking the user to choose between two options, indicating which one they prefer. This method is simple and intuitive, making it easier for users to provide feedback. It also helps reduce biases and inconsistencies, as the comparison is relative rather than absolute.
Let's explore this concept with an example involving customer support responses, where we compare responses generated by two models based on user satisfaction scores.
5. Pairwise comparisons
Here, we have two models generating responses to user queries. By comparing the satisfaction scores for each pair of responses, we can determine which model performs better.
For each response pair, we compare their scores. If model A's response has a higher score, it counts as a win for model A; otherwise, it counts as a win for model B. Since model B is treated as the baseline, model A must outperform B to win. Finally, the success rate for both models is calculated based on the number of wins.
This direct comparison provides clearer, more consistent feedback on model performance.
6. Ratings
Ratings, on the other hand, involve asking users to assign a score based on a predefined scale, such as 1 to 5 stars. While ratings can provide more information per feedback instance, they are also more prone to biases and inconsistencies, as users may interpret scales differently.
If we ask a user to rate movies from 1 to 5 stars, they might give Movie A a 4-star rating and Movie B a 3-star rating. While this provides more granular feedback, it can also introduce subjectivity and bias.
7. Psychological factors
To gather high-quality feedback, it's crucial to reduce bias from psychological factors.
Human decision-making is complex, and factors like cognitive biases play a big role. For example, how a question is framed can change the answer. This is known as the framing effect.
Then, there's the serial position effect. This means that people tend to favor items they see first or last, which can skew the feedback.
Finally, anchoring can also impact decisions. If a user sees certain information first, they can be influenced in how they judge everything else.
8. Guidelines for collecting high-quality feedback
We then have cognitive load: when users are tired or overwhelmed, their feedback becomes inconsistent. To mitigate this, we can design user-friendly interfaces and carefully design questions that simplify the decision-making process.
As we just saw, biases like framing and anchoring can distort feedback. To combat this, we can randomize the order of queries and provide training for labelers, helping them recognize and minimize these biases to improve the quality of collected data.
Finally, a challenge is the noise present in feedback data. Noise refers to random or irrelevant variations in the feedback that can obscure true preferences. We can filter out this noise using statistical techniques and collect diverse data to mitigate this risk.
9. Let's practice!
Let's practice these techniques!