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

• URL: http://arxiv.org/abs/2506.16406v1
• Date: Thu, 19 Jun 2025 15:38:21 GMT
• Title: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
• Authors: Zhiyuan Liang, Dongwen Tang, Yuhao Zhou, Xuanlei Zhao, Mingjia Shi, Wangbo Zhao, Zekai Li, Peihao Wang, Konstantin Schürholt, Damian Borth, Michael M. Bronstein, Yang You, Zhangyang Wang, Kai Wang,
• Abstract summary: textbfDrag-and-Drop LLMs (textitDnD) eliminates per-task training by mapping a handful of unlabeled task prompts directly to LoRA weight updates.<n>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.
• Score: 75.83625828306839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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{https://jerryliang24.github.io/DnD}{https://jerryliang24.github.io/DnD}.

Related papers

• SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity [52.88892280536302]
  We introduce SparseLoRA, a method that accelerates fine-tuning through contextual sparsity.<n>We show that SparseLoRA reduces computational cost by up to 2.2 times and a measured speedup of up to 1.6 times.
  arXiv  Detail & Related papers  (2025-06-19T17:53:34Z)
• Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning [16.99490636203893]
  We present textscRavan, an adaptive multi-head LoRA method that balances parameter efficiency and model expressivity.<n>Experiments on vision and language benchmarks show that textscRavan improves test accuracy by 2-8% over prior parameter-efficient baselines.
  arXiv  Detail & Related papers  (2025-06-05T20:28:02Z)
• WeightLoRA: Keep Only Necessary Adapters [79.89637596855]
  Low-rank adaptation ($texttLoRA$) adds trainable adapters to selected layers.<n>We propose a novel method, $textttWeightLoRA$, which overcomes this issue by adaptive selection of the most critical $textttLoRA$ heads.<n>We conduct experiments for a series of competitive benchmarks and DeBERTa, BART, and Llama models, comparing our method with different adaptive approaches.
  arXiv  Detail & Related papers  (2025-06-03T10:33:16Z)
• PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning [54.99373314906667]
  Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks.<n>As pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources.<n>We propose PointLoRA, a simple yet effective method that combines low-rank adaptation (LoRA) with multi-scale token selection to efficiently fine-tune point cloud models.
  arXiv  Detail & Related papers  (2025-04-22T16:41:21Z)
• MSPLoRA: A Multi-Scale Pyramid Low-Rank Adaptation for Efficient Model Fine-Tuning [5.412348391086257]
  We propose MSPLoRA, which introduces Global Shared LoRA, Mid-Level Shared LoRA, and Layer-Specific LoRA to capture global patterns, mid-level features, and fine-grained information.<n> Experiments on various NLP tasks demonstrate that MSPLoRA achieves more efficient adaptation and better performance while significantly reducing the number of trainable parameters.
  arXiv  Detail & Related papers  (2025-03-27T07:01:50Z)
• Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace [3.7049613588433497]
  Fine-tuning large language models (LLM) for various downstream tasks has become a new paradigm.<n>Low-Rank Adaptation (LoRA) is well-known for its parameter efficiency.<n>We propose a new method for.<n>Efficient decomposition- dubbed as DCFT- via deconvolution in subspace.
  arXiv  Detail & Related papers  (2025-03-03T11:15:50Z)
• SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model [11.90104174705911]
  We propose SSMLoRA (State Space Model Low-Rank Adaptation), an extension of Low-Rank Adaptation (LoRA) to interconnect low-rank matrices.<n>Our method achieves comparable performance to LoRA on the General Language Evaluation (GLUE) benchmark while using only half the parameters.
  arXiv  Detail & Related papers  (2025-02-07T14:22:35Z)
• LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning [4.616740762629019]
  Low-Rank Adaptation (LoRA) has sought to address the problem of handling the large updated parameters in full fine-tuning. We propose LoLDU, a suboptimal-Efficient Fine-Tuning (PEFT) approach that significantly reduces trainable parameters by 2600 times.
  arXiv  Detail & Related papers  (2024-10-17T14:51:17Z)
• LoRTA: Low Rank Tensor Adaptation of Large Language Models [70.32218116940393]
  Low Rank Adaptation (LoRA) is a popular Efficient Fine Tuning (PEFT) method.<n>We propose a higher-order Candecomp/Parafac (CP) decomposition, enabling a more compact and flexible representation.<n>Our method can achieve a reduction in the number of parameters while maintaining comparable performance.
  arXiv  Detail & Related papers  (2024-10-05T06:59:50Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks [10.266224162377371]
  Low-rank adaptation (LoRA) and its variants incur substantial storage and transmission costs. We introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules, and layers. VB-LoRA achieves extreme parameter efficiency while maintaining comparable or better performance compared to state-of-the-art PEFT methods.
  arXiv  Detail & Related papers  (2024-05-24T03:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.