As a machine learning system, Gemini operates within a feedback and reinforcement mechanism. Thus, when users interact with the Gem bot, if the latter gives wrong answers or does not answer, it can study these situations and change them in the future. This enables Gemini to enhance its response with better, related, and useful information over time. In a blog post on X, shows the feature of feedback.
It also means that Gem is capable of providing customized answers for a match based on your choices. This way, Gem can read your preferences and requirements from your usage and feedback, which means that it can deliver far more relevant results.
How does Gemini learn your preference using the feedback?
- Reinforcement Learning: This is a unique handling of feedback, as Gem is able to use the positive as well as negative feedback to manage its future feedback.
- Transfer Learning: The model can use learning from one task for another and hence enhance its capacity to generalize.
- Continuous Learning: Gemini is being continuously developed to make sure that it produces the most realistic floatation rates.
With the use of these learning techniques, Gemini can offer fresher, more relevant, and more specific responses in response to the queries from the users.
Rating the response
For Gemini Apps
- Directly in the app: After receiving a response, search for options like a good response or a bad response. It also may contain links to give extra comments or a complaint.
- Through the Gemini Help Center: In case you face a major concern or have a broad comment, you can go to the Help Center and report a problem.
For Gemini in Google Cloud
- Rate the response directly: The response usually receives a thumbs-up or thumbs-down kind of rating.
- Provide additional feedback: In case it is necessary, the students can submit the received prompt and the response they got to help the service improve the results in the future.