TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for Parameter-Efficient Fine-Tuning

• URL: http://arxiv.org/abs/2501.08008v1
• Date: Tue, 14 Jan 2025 10:51:31 GMT
• Title: TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
• Authors: Yao Liang, Yuwei Wang, Yi Zeng,
• Abstract summary: Fine-tuning Large Language Models (LLMs) is pivotal for achieving optimal performance across diverse downstream tasks. We propose Adaptive Low-Rank Adaptation (TriAdaptLoRA), a novel PEFT framework inspired by neuroscience principles. Experiments conducted on a variety of natural language understanding and generation tasks demonstrate that TriAdaptLoRA consistently outperforms existing PEFT methods.
• Score: 9.730075039461154
• License:
• Abstract: The fine-tuning of Large Language Models (LLMs) is pivotal for achieving optimal performance across diverse downstream tasks. However, while full fine-tuning delivers superior results, it entails significant computational and resource costs. Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA, address these challenges by reducing the number of trainable parameters, but they often struggle with rank adjustment efficiency and task-specific adaptability. We propose Triangular Adaptive Low-Rank Adaptation (TriAdaptLoRA), a novel PEFT framework inspired by neuroscience principles, which dynamically optimizes the allocation of trainable parameters. TriAdaptLoRA introduces three key innovations: 1) a triangular split of transformation matrices into lower and upper triangular components to maximize parameter utilization, 2) a parameter importance metric based on normalized Frobenius norms for efficient adaptation, and 3) an adaptive rank-growth strategy governed by dynamic thresholds, allowing flexible parameter allocation across training steps. Experiments conducted on a variety of natural language understanding and generation tasks demonstrate that TriAdaptLoRA consistently outperforms existing PEFT methods. It achieves superior performance, enhanced stability, and reduced computational overhead, particularly under linear threshold-driven rank growth. These results highlight its efficacy as a scalable and resource-efficient solution for fine-tuning LLMs.

Related papers

• EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition [2.5269004336032186]
  Efficient Weight-Decomposed Low-Rank Adaptation (EDoRA) is a novel PEFT method that decomposes pre-trained weights into magnitude and directional components. EDoRA achieves competitive or superior performance compared to state-of-the-art methods, such as LoRA and DoRA.
  arXiv  Detail & Related papers  (2025-01-21T11:42:09Z)
• OP-LoRA: The Blessing of Dimensionality [93.08208871549557]
  Low-rank adapters enable fine-tuning of large models with only a small number of parameters. They often pose optimization challenges, with poor convergence. We introduce an over- parameterized approach that accelerates training without increasing inference costs. We achieve improvements in vision-language tasks and especially notable increases in image generation.
  arXiv  Detail & Related papers  (2024-12-13T18:55:19Z)
• ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts [71.91042186338163]
  ALoRE is a novel PETL method that reuses the hypercomplex parameterized space constructed by Kronecker product to Aggregate Low Rank Experts. Thanks to the artful design, ALoRE maintains negligible extra parameters and can be effortlessly merged into the frozen backbone.
  arXiv  Detail & Related papers  (2024-12-11T12:31:30Z)
• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• LoRTA: Low Rank Tensor Adaptation of Large Language Models [70.32218116940393]
  Low Rank Adaptation (LoRA) is a popular Efficient Fine Tuning (PEFT) method. We propose a higher-order Candecomp/Parafac (CP) decomposition, enabling a more compact and flexible representation. Our method can achieve a reduction in the number of parameters while maintaining comparable performance.
  arXiv  Detail & Related papers  (2024-10-05T06:59:50Z)
• Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision [52.80792724919329]
  We introduce a novel framework named Adapter-X to improve fine-tuning in 2D image and 3D point cloud modalities. It is the first to outperform full fine-tuning in both 2D image and 3D point cloud modalities with significantly fewer parameters, i.e., only 0.20% and 1.88% of original trainable parameters for 2D and 3D classification tasks.
  arXiv  Detail & Related papers  (2024-06-05T08:26:44Z)
• Parameter-Efficient Fine-Tuning With Adapters [5.948206235442328]
  This research introduces a novel adaptation method utilizing the UniPELT framework as a base. Our method employs adapters, which enable efficient transfer of pretrained models to new tasks with minimal retraining of the base model parameters.
  arXiv  Detail & Related papers  (2024-05-09T01:40:38Z)
• Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning [11.037221461758806]
  Matrix-Transformation based Low-Rank Adaptation (MTLoRA) is inspired by the idea that the functions of the brain are shaped by its geometric structure. MTLoRA achieves an overall performance increase of about 1.0% across eight tasks.
  arXiv  Detail & Related papers  (2024-03-12T09:32:25Z)
• 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)

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.