Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Authors: name: Zhiyuan Liang, name: Dongwen Tang, name: Yuhao Zhou, name: Xuanlei Zhao, name: Mingjia Shi, name: Wangbo Zhao, name: Zekai Li, name: Peihao Wang, name: Konstantin Schürholt, name: Damian Borth, name: Michael M. Bronstein, name: Yang You, name: Zhangyang Wang, name: Kai Wang

Modern Parameter-Efficient Fine-Tuning (PEFT) methods such as low-rank
adaptation (LoRA) reduce the cost of customizing large language models (LLMs),
yet still require a separate optimization run for every downstream dataset. We
introduce Drag-and-Drop LLMs (\textit{DnD)}, a prompt-conditioned
parameter generator that eliminates per-task training by mapping a handful of
unlabeled task prompts directly to LoRA weight updates. A lightweight text
encoder distills each prompt batch into condition embeddings, which are then
transformed by a cascaded hyper-convolutional decoder into the full set of LoRA
matrices. Once trained in a diverse collection of prompt-checkpoint pairs, DnD
produces task-specific parameters in seconds, yielding i) up to
12,000times lower overhead than full fine-tuning, ii) average gains
up to 30\% in performance over the strongest training LoRAs on unseen
common-sense reasoning, math, coding, and multimodal benchmarks, and iii)
robust cross-domain generalization despite never seeing the target data or
labels. Our results demonstrate that prompt-conditioned parameter generation is
a viable alternative to gradient-based adaptation for rapidly specializing
LLMs. Our project is available at
https://jerryliang24.github.io/DnD{https://jerryliang24.github.io/DnD}.