Improving AI models requires collecting accurate user feedback,which can be difficult. The article explores more into the importance of collecting human feedback and discusses the need for creating an organized database to effectively store and use this valuable input. We hope to continuously improve our large language models, ensuring they deliver greater performance and match user expectations more effectively.

Design feedback mechanism for LLM applications

The article talks about how important human feedback is for making large language models better, especially using thumbs up and down. It explains the Pearson correlation coefficient, which measures how variables are related. The thumbs system is an easy way for users to show if responses are helpful or not, which encourages more feedback and keeps users engaged. This feedback helps improve training data and makes the models more accurate. The article includes examples in sentiment analysis for reviews and content moderation, showing how this feedback helps understand user opinions and filter out bad content on sites like YouTube and Twitter.

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