Aligning Language Models from User Interactions

Researchers propose a method using self-distillation to improve language model performance by learning from multi-turn user interactions, enhancing alignment and instruction-following abilities without degrading other capabilities. This approach allows models to adapt continuously to individual users through natural conversations, enabling personalization and improved accuracy in responses.

Read the full article at arXiv cs.CL (NLP)

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