Incorporating diverse feedback sources

1. Incorporating diverse feedback sources

In this video, we will identify the benefits of diversifying human feedback. We will also learn how to incorporate feedback from multiple sources, and identify strategies for managing conflicting feedback.

2. Improved model generalization

Diverse feedback sources allow the model to generate responses that better represent different viewpoints. This leads to outputs that are more adaptable to a wider range of use cases.

3. Reduced bias

Individual biases, such as such as those stemming from cultural stereotypes or gender assumptions, can also be mitigated, to align the model's responses more closely with human values. For instance, having similar representation of females and males is often used to reduce bias.

4. Better alignment with human values

Similarly, the model's outputs can be more closely aligned with complex human preferences, aligning better with different cultures and backgrounds.

5. Enhanced adaptability

Adaptability is also achieved through diversification, as the model responds to a wider range of user needs and preferences, reflecting different viewpoints more closely.

6. Increased robustness

Finally, when presented with different types of inputs and contexts, the model becomes more resilient and adaptive. Improving the overall quality of its outputs.

7. Integrating preference data from multiple sources

Now, let's consider how these elements come together in a dataset. The dataset was created by three expert curators - a journalist, a social media influencer, and a marketing professional. They've each labeled paired text samples as 'chosen' and 'rejected.' Each chosen-rejected pair has a unique 'id' within the 'source' subgroups, and every entry showcases a preferred news headline each expert found most compelling.

8. Majority voting

We can efficiently organize the dataset by grouping entries according to their 'id' key. We then employ a majority voting function to determine the preferred sequence from each group. The function uses the 'Counter' class, which takes an iterable input with ('chosen', 'rejected') pairs counting the occurrences of each unique pair. The max() function then uses a key corresponding to those counts to get the majority pair.

9. Unreliable preference data sources

Let's now examine another dataset with the same three experts. As illustrated in the slide, the data from 'Marketing Professional', frequently conflicts with the data from data sources 'Journalist' and 'Social Media Influencer'. For instance, 'Marketing Professional' has selected their preference for 'Weather patterns changing, scientists unsure why', whereas the other two sources have opted for 'Study shows climate change accelerating faster than predicted'.

10. Unreliable preference data sources

To identify potentially unreliable data sources, we can implement an algorithm that evaluates disagreement with the majority vote. First, we group all data by a unique id and calculate the majority vote for each group. Next, we start a count to keep track of how many times each source disagrees with the consensus. We then go through each row of the dataset. For every row, we check whether the data source's decision aligns with the majority vote for that specific id. If it doesn't, we increase the disagreement count. Finally, the source with the highest number of disagreements is potentially unreliable.

11. Let's practice!

And now let's practice with some common challenges in the integration of feedback sources.

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.