What is RLHF and Supervised Learning with Human Feedback?
Reinforcement Learning from Human Feedback (RLHF) is an advanced AI training technique that combines machine learning with human input. It enables AI models to learn and improve based on real-world interactions and feedback from users. Meanwhile, Supervised Learning with Human Feedback involves an AI model generating outputs that are reviewed and refined by humans, which are then used to improve the model’s future performance.
Key Components of RLHF:
- Initial AI Model: A pre-trained language model serves as the starting point.
- Human Feedback: Users provide feedback on the AI’s responses.
- Reward Model: This system learns to predict which AI outputs humans will prefer.
- Fine-tuning: The AI model is updated based on the reward model’s predictions.
Key Components of Supervised Learning with Human Feedback:
- AI Output Generation: The AI generates outputs based on learned data, such as FAQs from past conversations.
- Human Review: Operators or experts review and refine these outputs.
- Model Improvement: The AI model is updated or retrained using the refined outputs, enhancing future performance.
How RLHF is Implemented in Our Chatbot
Our YourGPT Chatbot uses RLHF to continuously enhance its performance and user experience. Here’s how we’ve implemented this technology:
1. Learning from Feedback
We’ve built a robust feedback system for our users, or chatbot members, where the feedback mechanism is implemented for visitors interacting with the chatbot. Chatbot members can view this feedback and use it to improve the AI:
- Feedback Collection: Visitors can provide feedback during chat sessions with the chatbot across any integration.
- Negative Feedback Analysis: Chatbot members cam further pay attention to queries that receive negative feedback, using them as learning opportunities.
- Continuous Improvement: Your chatbot is continuously fine-tuned based on the feedback it receives, with a particular focus on addressing areas identified as needing improvement.
How Supervised Learning is Implemented in Our Chatbot
YourGPT Chatbot also utilizes Supervised Learning with Human Feedback to improve its functionality. Here’s how we’ve integrated this approach:
1. Learning from Previous Conversations
YourGPT Chatbot doesn’t just learn from individual interactions—it also learns from the broader context of conversations:
- Contextual Learning: The AI analyzes past chat interactions to generate FAQs and other outputs, which are then reviewed by chatbot members.
- Human-Guided Improvement: They can further refine these AI-generated outputs, ensuring they meet quality standards and relevance.
- Final Training: The refined content is used to retrain the AI model, enhancing its ability to generate accurate and useful FAQs in the future.
2. Unresolved Query Tracking
The AI identifies and logs queries it struggles with during interactions. These unresolved queries are then reviewed by chatbot members, who provide targeted improvements.