2 papers
cs.LG2026
Refinement Provenance Inference: Detecting LLM-Refined Training Prompts from Model Behavior
Bo Yin, Qi Li, Runpeng Yu +1
Instruction tuning increasingly relies on LLM-based prompt refinement, where prompts in the training corpus are selectively rewritten by an external refiner to improve clarity and…
cs.LG2025
Don't Forget the Nonlinearity: Unlocking Activation Functions in Efficient Fine-Tuning
Bo Yin, Xingyi Yang, Xinchao Wang
Existing parameter-efficient fine-tuning (PEFT) methods primarily adapt weight matrices while keeping activation functions fixed. We introduce \textbf{NoRA}, the first PEFT framewo…