Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning

• URL: http://arxiv.org/abs/2406.17740v1
• Date: Tue, 25 Jun 2024 17:26:05 GMT
• Title: Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
• Authors: Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi,
• Abstract summary: We propose a framework for efficient parameter fine-tuning (PEFT) based on structured unrestricted-rank matrices (SURM) SURMs achieve 5-7% accuracy gains on various image classification tasks while replacing low-rank matrices in LoRA. It also results in up to 12x reduction of the number of parameters in adapters (with virtually no loss in quality) on the GLUE benchmark.
• Score: 38.80020737321214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei et al., 2022). However, fine-tuning these models for downstream tasks is expensive due to their large parameter counts. Parameter-efficient fine-tuning (PEFT) approaches have emerged as a viable alternative by allowing us to fine-tune models by updating only a small number of parameters. In this work, we propose a general framework for parameter efficient fine-tuning (PEFT), based on structured unrestricted-rank matrices (SURM) which can serve as a drop-in replacement for popular approaches such as Adapters and LoRA. Unlike other methods like LoRA, SURMs provides more flexibility in finding the right balance between compactness and expressiveness. This is achieved by using low displacement rank matrices (LDRMs), which hasn't been used in this context before. SURMs remain competitive with baselines, often providing significant quality improvements while using a smaller parameter budget. SURMs achieve 5-7% accuracy gains on various image classification tasks while replacing low-rank matrices in LoRA. It also results in up to 12x reduction of the number of parameters in adapters (with virtually no loss in quality) on the GLUE benchmark.

Related papers

• LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models [21.889177019111525]
  Training large models with millions or even billions of parameters from scratch incurs substantial computational costs. We use Low-Rank Adaptation (LoRA) to adapt only a reduced number of parameters to specific tasks with gradient-baseds. We propose robust approaches that work well across a vast range of well-established computer vision and language models.
  arXiv  Detail & Related papers  (2024-10-15T12:41:31Z)
• LoRTA: Low Rank Tensor Adaptation of Large Language Models [70.32218116940393]
  Low Rank Adaptation (LoRA) is a popular Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. We propose a novel approach that employs a low rank tensor parametrization for model updates. Our method is both efficient and effective for fine-tuning large language models, achieving a substantial reduction in the number of parameters while maintaining comparable performance.
  arXiv  Detail & Related papers  (2024-10-05T06:59:50Z)
• LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models [3.7049613588433497]
  Low-Rank Adaptation (LoRA) significantly reduces the number of trainable parameters for fine-tuning. We extend the LoRA to multiple scales, dubbed as LoRA$2$.
  arXiv  Detail & Related papers  (2024-08-13T12:31:30Z)
• Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models [108.08773541490191]
  Pre-trained Language models (PLMs) have a huge amount of parameters, fine-tuning them is often expensive and time consuming. It is necessary to adopt a parameter-efficient approach to reduce parameters of PLMs in fine-tuning without compromising their performance in downstream tasks. In this paper, we design a novel adapter which only acts on self-attention outputs in PLMs.
  arXiv  Detail & Related papers  (2024-07-04T18:21:28Z)
• LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters [11.23006032094776]
  We introduce LoRA-XS, a novel low-rank adaptation method that considerably reduces the trainable parameters while showing superior or competitive performance. LoRA-XS achieves a remarkable reduction of trainable parameters by over 100x in 7B models compared to LoRA.
  arXiv  Detail & Related papers  (2024-05-27T19:07:13Z)
• DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution [28.589498108609202]
  Low-Rank Adaptation (LoRA) relies on a bypass framework that ignores the differential parameter budget requirements across weight matrices. DoRA decomposes high-rank LoRA layers into structured single-rank components, allowing for dynamic pruning of parameter budget. Experimental results demonstrate that DoRA can achieve competitive performance compared with LoRA and full model fine-tuning.
  arXiv  Detail & Related papers  (2024-05-27T17:02:27Z)
• 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)
• Parameter-efficient Tuning of Large-scale Multimodal Foundation Model [68.24510810095802]
  We propose A graceful prompt framework for cross-modal transfer (Aurora) to overcome these challenges. Considering the redundancy in existing architectures, we first utilize the mode approximation to generate 0.1M trainable parameters to implement the multimodal prompt tuning. A thorough evaluation on six cross-modal benchmarks shows that it not only outperforms the state-of-the-art but even outperforms the full fine-tuning approach.
  arXiv  Detail & Related papers  (2023-05-15T06:40:56Z)
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning [143.23123791557245]
  Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. We propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA.
  arXiv  Detail & Related papers  (2023-03-18T22:36:25Z)

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.