Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform

• URL: http://arxiv.org/abs/2410.09103v1
• Date: Wed, 9 Oct 2024 16:07:42 GMT
• Title: Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform
• Authors: Yixian Shen, Qi Bi, Jia-Hong Huang, Hongyi Zhu, Anuj Pathania,
• Abstract summary: We propose a novel Selective Discrete Cosine Transformation (sDCTFT) fine-tuning scheme to push this frontier. Its general idea is to exploit the superior energy compaction and decorrelation properties of DCT. Experiments on four benchmark datasets demonstrate the superior accuracy, reduced computational cost, and lower storage requirements.
• Score: 10.565509997395504
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the era of large language models, parameter-efficient fine-tuning (PEFT) has been extensively studied. However, these approaches usually rely on the space domain, which encounters storage challenges especially when handling extensive adaptations or larger models. The frequency domain, in contrast, is more effective in compressing trainable parameters while maintaining the expressive capability. In this paper, we propose a novel Selective Discrete Cosine Transformation (sDCTFT) fine-tuning scheme to push this frontier. Its general idea is to exploit the superior energy compaction and decorrelation properties of DCT to improve both model efficiency and accuracy. Specifically, it projects the weight change from the low-rank adaptation into the discrete cosine space. Then, the weight change is partitioned over different levels of the discrete cosine spectrum, and the most critical frequency components in each partition are selected. Extensive experiments on four benchmark datasets demonstrate the superior accuracy, reduced computational cost, and lower storage requirements of the proposed method over the prior arts. For instance, when performing instruction tuning on the LLaMA3.1-8B model, sDCTFT outperforms LoRA with just 0.05M trainable parameters compared to LoRA's 38.2M, and surpasses FourierFT with 30\% less trainable parameters. The source code will be publicly available.

Related papers

• IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models [68.55148272295916]
  We propose IntLoRA, to push the efficiency limits by using integer type (INT) low-rank parameters to adapt the quantized diffusion models. IntLoRA offers three key advantages: (i) for fine-tuning, the pre-trained weights are quantized, reducing memory usage; (ii) for storage, both pre-trained and low-rank weights are in INT which consumes less disk space; (iii) for inference, IntLoRA weights can be naturally merged into quantized pre-trained weights through efficient integer multiplication or bit-shifting.
  arXiv  Detail & Related papers  (2024-10-29T05:50:17Z)
• 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)
• Propulsion: Steering LLM with Tiny Fine-Tuning [0.0]
  We propose Propulsion, a novel parameter efficient fine-tuning (PEFT) method to optimize task-specific performance. Inspired by the concept of controlled adjustments in physical motion, Propulsion selectively re-scales specific dimensions of a pre-trained model. Our theoretical analysis, supported by Neural Tangent Kernel (NTK) theory, shows that Propulsion approximates the performance of full fine-tuning with far fewer trainable parameters.
  arXiv  Detail & Related papers  (2024-09-17T06:51:59Z)
• ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections [59.839926875976225]
  We propose the ETHER transformation family, which performs Efficient fineTuning via HypErplane Reflections. In particular, we introduce ETHER and its relaxation ETHER+, which match or outperform existing PEFT methods with significantly fewer parameters.
  arXiv  Detail & Related papers  (2024-05-30T17:26:02Z)
• Parameter-Efficient Fine-Tuning with Discrete Fourier Transform [26.563344030824414]
  Low-rank adaptation(LoRA) has recently gained much interest in fine-tuning foundation models. We introduce FourierFT, which treats $Delta W$ as a matrix in the spatial domain and learns only a small fraction of its spectral coefficients. Our method shows comparable or better performance with fewer parameters than LoRA on various tasks.
  arXiv  Detail & Related papers  (2024-05-05T17:15:24Z)
• SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models [28.764782216513037]
  Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning. We propose a method called SLoRA, which overcomes the key limitations of LoRA in high heterogeneous data scenarios. Our experimental results demonstrate that SLoRA achieves performance comparable to full fine-tuning.
  arXiv  Detail & Related papers  (2023-08-12T10:33:57Z)
• Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning [91.5113227694443]
  We propose a novel visual. sensuous-aware fine-Tuning (SPT) scheme. SPT allocates trainable parameters to task-specific important positions. Experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods.
  arXiv  Detail & Related papers  (2023-03-15T12:34:24Z)
• Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning [126.84770886628833]
  Existing finetuning methods either tune all parameters of the pretrained model (full finetuning) or only tune the last linear layer (linear probing) We propose a new parameter-efficient finetuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance full finetuning.
  arXiv  Detail & Related papers  (2022-10-17T08:14:49Z)
• LoRA: Low-Rank Adaptation of Large Language Models [71.75808607987281]
  Low-Rank Adaptation, or LoRA, freezes the pre-trained model weights and injects trainable rank decomposition into each layer of the Transformer architecture. For GPT-3, LoRA can reduce the number of trainable parameters by 10,000 times and the computation hardware requirement by 3 times compared to full fine-tuning.
  arXiv  Detail & Related papers  (2021-06-17T17:37:18Z)

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.