Semantic-guided LoRA Parameters Generation

• URL: http://arxiv.org/abs/2509.10535v1
• Date: Fri, 05 Sep 2025 14:43:41 GMT
• Title: Semantic-guided LoRA Parameters Generation
• Authors: Miaoge Li, Yang Chen, Zhijie Rao, Can Jiang, Jingcai Guo,
• Abstract summary: Low-Rank Adaptation (LoRA) has demonstrated strong generalization capabilities across a variety of tasks for efficiently fine-tuning AI models.<n>SG-LoRA is the first of its kind framework to efficiently produce user-specific LoRA without additional training on user tasks or access to user-specific data.<n>SG-LoRA enables the real-time construction of LoRA models aligned with individual intents by distilling knowledge from prominent LoRA experts.
• Score: 22.648880814012184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Rank Adaptation (LoRA) has demonstrated strong generalization capabilities across a variety of tasks for efficiently fine-tuning AI models, especially on resource-constrained edges. However, in real-world applications, edge users often exhibit task-specific preferences that are difficult to handle with a unified model trained under a closed-world assumption, and the challenge may further increase when there are significant domain shifts between training and deployment. Meanwhile, retraining/fine-tuning models for each user is also impractical due to its cost-intensive nature and privacy concerns over raw data utilization from edges. To address these challenges, we propose Semantic-guided LoRA Parameter Generation (SG-LoRA), the first of its kind framework to efficiently produce user-specific LoRA parameters without any additional training on user tasks or access to user-specific data. Concretely, SG-LoRA uses task descriptions as the semantic bridge, measuring their proximity to a set of known expert tasks in a shared embedding space. Based on this semantic guidance, it models the target task's LoRA parameter distribution to generate high-performing parameters for novel tasks. SG-LoRA enables the real-time construction of LoRA models aligned with individual intents by distilling knowledge from prominent LoRA experts and, meanwhile, offering a privacy-preserving solution for personalized model adaptation in a novel zero-shot open-world setting proposed in this work. Extensive experiments on multiple challenging tasks confirm the superior performance and remarkable adaptability of SG-LoRA. Code is available at https://github.com/keepgoingjkg/SG-LoRA.

Related papers

• DR-LoRA: Dynamic Rank LoRA for Mixture-of-Experts Adaptation [26.24723718425076]
  Mixture-of-Experts (MoE) has become a prominent paradigm for scaling Large Language Models (LLMs)<n>We propose a Dynamic Rank LoRA framework named DR-LoRA, which dynamically grows expert LoRA ranks during fine-tuning based on task-specific demands.<n>Experiments on multiple benchmarks demonstrate that DR-LoRA consistently outperforms standard LoRA and static allocation strategies under the same parameter budget.
  arXiv  Detail & Related papers  (2026-01-08T10:58:51Z)
• SEQR: Secure and Efficient QR-based LoRA Routing [53.52716967527183]
  Low-Rank Adaptation (LoRA) has become a standard technique for parameter-efficient fine-tuning of large language models.<n> Efficiently selecting the correct LoRA adapter for a given input remains a challenge.<n>We introduce SEQR, an unsupervised LoRA routing algorithm designed to maximize efficiency while providing strict routing guarantees.
  arXiv  Detail & Related papers  (2025-09-22T17:59:38Z)
• Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning [12.733972494875713]
  Federated Learning (FL) offers a privacy-preserving solution, but faces challenges with computational constraints.<n>Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient fine-tuning approach.<n>We propose FedLEASE, a novel framework that adaptively clusters clients based on representation similarity to allocate and train domain-specific LoRA experts.
  arXiv  Detail & Related papers  (2025-09-18T15:43:33Z)
• LoRA-Gen: Specializing Large Language Model via Online LoRA Generation [68.01864057372067]
  We propose the LoRA-Gen framework to generate LoRA parameters for edge-side models based on task descriptions.<n>We merge the LoRA parameters into the edge-side model to achieve flexible specialization.<n>Our method facilitates knowledge transfer between models while significantly improving the inference efficiency of the specialized model.
  arXiv  Detail & Related papers  (2025-06-13T10:11:01Z)
• In-Context Meta LoRA Generation [61.690065588534296]
  Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning.<n>We propose In-Context Meta LoRA (ICM-LoRA), a novel approach that efficiently achieves task-specific customization of large language models.<n>ICM-LoRA enables more accurate LoRA parameter reconstruction than current parameter reconstruction methods.
  arXiv  Detail & Related papers  (2025-01-29T13:12:01Z)
• MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning [74.43869839954168]
  We propose MTL-LoRA, which retains the advantages of low-rank adaptation while significantly enhancing MTL capabilities.<n> MTL-LoRA augments LoRA by incorporating additional task-adaptive parameters that differentiate task-specific information and capture shared knowledge.<n>This approach enables pre-trained models to jointly adapt to different target domains with a limited number of trainable parameters.
  arXiv  Detail & Related papers  (2024-10-12T08:32:26Z)
• Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning [65.31677646659895]
  Large language models demonstrate impressive performance on downstream tasks, yet they require extensive resource consumption when fully fine-tuning all parameters.<n>We propose a framework to clearly define task-specific directions (TSDs) and explore their properties and practical utilization challenges.<n>We then introduce a novel approach, LoRA-Dash, which aims to maximize the impact of TSDs during the fine-tuning process.
  arXiv  Detail & Related papers  (2024-09-02T08:10:51Z)
• Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation [50.837277466987345]
  We focus on the field of large language models (LLMs) for recommendation. We propose RecLoRA, which incorporates a Personalized LoRA module that maintains independent LoRAs for different users. We also design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces.
  arXiv  Detail & Related papers  (2024-08-07T04:20:28Z)
• One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning [34.109808214968176]
  Generalized LoRA (GLoRA) is an advanced approach for universal parameter-efficient fine-tuning tasks. It employs a generalized prompt module to optimize pre-trained model weights and adjust intermediate activations. GLoRA exhibits strong transfer learning, few-shot learning and domain generalization abilities.
  arXiv  Detail & Related papers  (2023-06-13T17:59:32Z)

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.