Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

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 \textbf{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 \textbf{12,000$\times$} lower overhead than full fine-tuning, ii) average gains up to \textbf{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 \href{this https URL}{this https URL}.

@article{liang2025_2506.16406,
  title={ Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights },
  author={ Zhiyuan Liang and Dongwen Tang and Yuhao Zhou and Xuanlei Zhao and Mingjia Shi and Wangbo Zhao and Zekai Li and Peihao Wang and Konstantin Schürholt and Damian Borth and Michael M. Bronstein and Yang You and Zhangyang Wang and Kai Wang },
  journal={arXiv preprint arXiv:2506.16406},
  year={ 2025 }
}