Dan Zhang

This survey by Tsinghua University's Knowledge Engineering Group systematically reviews parameter-efficient fine-tuning (PEFT) for foundation models (FMs), organizing methods into five categories—selective, additive, prompt, reparameterization, and hybrid—and reviewing their applications across five model structures (LLMs, vision foundation models, vision-language models, visual content generation models, and multimodal foundation models), while deriving three trend observations from Semantic Scholar citation counts (PEFT growing broadly; LLMs and VFMs dominating; MFMs relatively underexplored) and identifying reliability, interpretability, and unified benchmarks as future directions.

This survey by Tsinghua University's Knowledge Engineering Group systematically reviews parameter-efficient fine-tuning (PEFT) for foundation models (FMs), organizing methods into five categories—selective, additive, prompt, reparameterization, and hybrid—and reviewing their applications across five model structures (LLMs, vision foundation models, vision-language models, visual content generation models, and multimodal foundation models), while deriving three trend observations from Semantic Scholar citation counts (PEFT growing broadly; LLMs and VFMs dominating; MFMs relatively underexplored) and identifying reliability, interpretability, and unified benchmarks as future directions.