Michael Kwok-Po Ng

This work proposes GenFT, a W0-conditioned parameter-efficient fine-tuning method in which a deterministic generator produces task-specific updates ΔW by applying row and column transformations to the pretrained weights W0 together with a shared-specific decomposition, reporting competitive or better average performance on GLUE (RoBERTaBase, 85.87% average with 0.24M parameters), VTAB-1K (ViT-B/16, 74.50% average with 0.27M parameters) and FGVC (90.38% average with 0.29M parameters), plus a perplexity pilot study on LLaMA-7B with an Alpaca subset.

This work proposes GenFT, a W0-conditioned parameter-efficient fine-tuning method in which a deterministic generator produces task-specific updates ΔW by applying row and column transformations to the pretrained weights W0 together with a shared-specific decomposition, reporting competitive or better average performance on GLUE (RoBERTaBase, 85.87% average with 0.24M parameters), VTAB-1K (ViT-B/16, 74.50% average with 0.27M parameters) and FGVC (90.38% average with 0.29M parameters), plus a perplexity pilot study on LLaMA-7B with an Alpaca subset.