Personalized LoRA for Human-Centered Text Understanding

• URL: http://arxiv.org/abs/2403.06208v1
• Date: Sun, 10 Mar 2024 13:04:54 GMT
• Title: Personalized LoRA for Human-Centered Text Understanding
• Authors: You Zhang, Jin Wang, Liang-Chih Yu, Dan Xu, Xuejie Zhang
• Abstract summary: We introduce personalized LoRA (PLoRA) with a plug-and-play framework for the HCTU task. PLoRA is effective, parameter-efficient and dynamically deploying in PLMs. Experiments conducted on four benchmark datasets show that the proposed method outperforms existing methods in full/few/zero-shot learning scenarios.
• Score: 15.704545145736676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered text understanding (HCTU) is challenging since user tokens are million-level in most personalized applications and do not have concrete explicit semantics. A standard and parameter-efficient approach (e.g., LoRA) necessitates memorizing numerous suits of adapters for each user. In this work, we introduce a personalized LoRA (PLoRA) with a plug-and-play (PnP) framework for the HCTU task. PLoRA is effective, parameter-efficient, and dynamically deploying in PLMs. Moreover, a personalized dropout and a mutual information maximizing strategies are adopted and hence the proposed PLoRA can be well adapted to few/zero-shot learning scenarios for the cold-start issue. Experiments conducted on four benchmark datasets show that the proposed method outperforms existing methods in full/few/zero-shot learning scenarios for the HCTU task, even though it has fewer trainable parameters. For reproducibility, the code for this paper is available at: https://github.com/yoyo-yun/PLoRA.

Related papers

• Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights [75.83625828306839]
  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.
  arXiv  Detail & Related papers  (2025-06-19T15:38:21Z)
• Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs [12.995753143157083]
  Contrastive LoRA Decoding (CoLD) is a novel decoding framework designed to maximize the use of task-specific knowledge in LoRA-adapted models.<n>CoLD achieves up to a 5.54% increase in task accuracy while reducing end-to-end latency by 28%.
  arXiv  Detail & Related papers  (2025-05-20T17:11:18Z)
• A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning [0.6906005491572401]
  We propose a method for allocating expert numbers based on parameter sensitivity LoRA-SMoE.<n> Experimental results demonstrate that our LoRA-SMoE approach can enhance model performance while reducing the number of trainable parameters.
  arXiv  Detail & Related papers  (2025-05-06T13:22:46Z)
• 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. As pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources. 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)
• Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA [14.786030311860145]
  PF2LoRA is a new personalized federated fine-tuning algorithm built on a novel emphautomatic rank learning approach via two-level LoRA Our experiments on natural language understanding and generation tasks demonstrate that PF2LoRA significantly outperforms existing federated fine-tuning methods.
  arXiv  Detail & Related papers  (2025-03-05T21:41:03Z)
• Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning [22.748835458594744]
  We introduce Retrieval-based. Ensemble (RPE), a new method that creates a vectorized database of. Low-Rank Adaptations (LoRAs) RPE minimizes the need for extensive training and eliminates the requirement for labeled data, making it particularly effective for zero-shot learning. RPE is well-suited for privacy-sensitive domains like healthcare, as it modifies model parameters without accessing raw data.
  arXiv  Detail & Related papers  (2024-10-13T16:28:38Z)
• 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 multi-task learning capabilities. MTL-LoRA augments LoRA by incorporating additional task-adaptive parameters that differentiate task-specific information. This approach enables large language models (LLMs) pre-trained on general corpus to adapt to different target task domains with a limited number of trainable parameters.
  arXiv  Detail & Related papers  (2024-10-12T08:32:26Z)
• 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)
• Reinforced Prompt Personalization for Recommendation with Large Language Models [24.360796133889156]
  This paper introduces the concept of instance-wise prompting to personalize discrete prompts for individual users. We propose Reinforced Prompt Personalization (RPP) to optimize the four patterns in prompts using multi-agent reinforcement learning (MARL) Experimental results demonstrate the superiority of RPP/RPP+ over traditional recommender models.
  arXiv  Detail & Related papers  (2024-07-24T09:24:49Z)
• MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning [71.50432879573614]
  Low-rank adaptation (LoRA) is based on the idea that the adaptation process is intrinsically low-dimensional. We present MELoRA, a mini-ensemble low-rank adapters that uses fewer trainable parameters while maintaining a higher rank. Our experimental results show that, compared to LoRA, MELoRA achieves better performance with 8 times fewer trainable parameters on natural language understanding tasks and 36 times fewer trainable parameters on instruction following tasks.
  arXiv  Detail & Related papers  (2024-02-27T07:14:12Z)
• PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation [65.268245109828]
  We introduce PRILoRA, which linearly allocates a different rank for each layer, in an increasing manner, and performs pruning throughout the training process. We validate the effectiveness of PRILoRA through extensive experiments on eight GLUE benchmarks, setting a new state of the art.
  arXiv  Detail & Related papers  (2024-01-20T20:25:17Z)
• PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning [41.984652077669104]
  Experimental results on standard datasets indicate that our method outperforms the state-of-the-art approaches significantly. Our method exhibits strong robustness and superiority in different settings and degrees of data heterogeneity.
  arXiv  Detail & Related papers  (2024-01-04T06:46:19Z)
• Sparse Low-rank Adaptation of Pre-trained Language Models [79.74094517030035]
  We introduce sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. Our approach strengthens the representation power of LoRA by initializing it with a higher rank, while efficiently taming a temporarily increased number of parameters. Our experimental results demonstrate that SoRA can outperform other baselines even with 70% retained parameters and 70% training time.
  arXiv  Detail & Related papers  (2023-11-20T11:56:25Z)
• SiRA: Sparse Mixture of Low Rank Adaptation [63.926732717719354]
  We investigate the importance of leveraging "sparse" computation and propose SiRA: sparse mixture of low rank. Specifically it enforces the top $k$ experts routing with a capacity limit restricting the maximum number of tokens each expert can process.
  arXiv  Detail & Related papers  (2023-11-15T18:15:37Z)
• IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning [15.964205804768163]
  IncreLoRA is an incremental parameter allocation method that adaptively adds trainable parameters during training. We conduct extensive experiments on GLUE to demonstrate the effectiveness of IncreLoRA.
  arXiv  Detail & Related papers  (2023-08-23T10:08:10Z)

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.