Principal Component Analysis

Machine Learning

Fine-tuning methods (like DPO, RLHF) aligning models with human preferences using preferred vs. non-preferred output data.

Detailed Explanation

Preference Tuning is a machine learning technique that refines models by aligning their outputs with human preferences. Using methods like Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF), the model learns to favor responses deemed more desirable by humans, improving relevance, safety, and user satisfaction. It enhances model behavior by leveraging human judgments during training.

Use Cases

•Improve chatbot responses by applying Preference Tuning to better align outputs with user preferences and increase satisfaction.

Related Terms

Other terms in the Machine Learning category