Different Feedback, Different Updates: Selective Self-Learning from User Interactions for Large Language Models
arXiv:2608.09109v2 Announce Type: replace Abstract: User feedback offers natural supervision for persistent LLM improvement, but a single message may support multiple behavioral changes with different scopes of generalization. We introduce SLIFT, a selective self-learning framework built on a task-relative view of user feedback. SLIFT decomposes each feedback message into atomic components and interprets each component relative to the original task as Fix, Spec, or Null: requirements for task v…