Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

• URL: http://arxiv.org/abs/2410.19878v2
• Date: Fri, 01 Nov 2024 03:26:07 GMT
• Title: Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
• Authors: Luping Wang, Sheng Chen, Linnan Jiang, Shu Pan, Runze Cai, Sen Yang, Fei Yang,
• Abstract summary: Large models require substantial computational resources and GPU memory to operate. Efficient Fine-Tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. This review mainly introduces the preliminary knowledge of PEFT, the core ideas and principles of various PEFT algorithms, the applications of PEFT, and potential future research directions.
• Score: 17.904502959675337
• License:
• Abstract: The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial computational resources and GPU memory to operate. When adapting large models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine-tuning on hardware platforms with limited computational power and GPU memory. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. Specifically, PEFT adjusts the parameters of pre-trained large models to adapt to specific tasks or domains, minimizing the introduction of additional parameters and the computational resources required. This review mainly introduces the preliminary knowledge of PEFT, the core ideas and principles of various PEFT algorithms, the applications of PEFT, and potential future research directions. By reading this review, we believe that interested parties can quickly grasp the PEFT methodology, thereby accelerating its development and innovation.

Related papers

• Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models [18.877891285367216]
  A class of parameter-efficient fine-tuning (PEFT) aims to mitigate computational challenges by selectively fine-tuning only a small fraction of the model parameters. We introduce $textID3$, a novel selective PEFT method that calculates parameter importance continually and dynamically unmasks parameters. We analytically show that $textID3$ reduces the number of gradient updates by a factor of two, enhancing computational efficiency.
  arXiv  Detail & Related papers  (2024-08-26T17:58:53Z)
• Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models [68.23649978697027]
  Forecast-PEFT is a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks. Forecast-FT further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods.
  arXiv  Detail & Related papers  (2024-07-28T19:18:59Z)
• 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: A Comprehensive Analysis Across Applications [0.7421845364041001]
  The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging. Traditional fine-tuning methods, involving adjustments to all parameters, face challenges due to high computational and memory demands. This review examines Efficient Fine-Tuning (PEFT) techniques, which selectively update parameters to balance computational efficiency with performance.
  arXiv  Detail & Related papers  (2024-04-21T02:26:15Z)
• Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey [18.00772798876708]
  Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks. PEFT refers to the process of adjusting the parameters of a pre-trained large model to adapt it to a specific task or domain. We present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead.
  arXiv  Detail & Related papers  (2024-03-21T17:55:50Z)
• Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment [12.674032145667763]
  We present a comprehensive and systematic review of Efficient Fine-Tuning (PEFT) methods for pretrained language models (PLMs) PEFT offers an effective solution by reducing the number of fine-tuning parameters and memory usage while achieving comparable performance to full fine-tuning. We conduct experiments using several representative PEFT methods to better understand their effectiveness in parameter efficiency and memory efficiency.
  arXiv  Detail & Related papers  (2023-12-19T13:31:24Z)
• Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models [109.06052781040916]
  We introduce a technique to enhance the inference efficiency of parameter-shared language models. We also propose a simple pre-training technique that leads to fully or partially shared models. Results demonstrate the effectiveness of our methods on both autoregressive and autoencoding PLMs.
  arXiv  Detail & Related papers  (2023-10-19T15:13:58Z)
• Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models [46.42092771753465]
  We introduce Point-PEFT, a novel framework for adapting point cloud pre-trained models with minimal learnable parameters. Specifically, for a pre-trained 3D model, we freeze most of its parameters, and only tune the newly added PEFT modules on downstream tasks.
  arXiv  Detail & Related papers  (2023-10-04T16:49:36Z)
• Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture [68.13678918660872]
  We design a more capable parameter-sharing architecture based on matrix product operator (MPO) MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts. Our architecture shares the central tensor across all layers for reducing the model size.
  arXiv  Detail & Related papers  (2023-03-27T02:34:09Z)
• Parameter-Efficient Sparsity for Large Language Models Fine-Tuning [63.321205487234074]
  We propose a. sparse-efficient Sparse Training (PST) method to reduce the number of trainable parameters during sparse-aware training. Experiments with diverse networks (i.e., BERT, RoBERTa and GPT-2) demonstrate PST performs on par or better than previous sparsity methods.
  arXiv  Detail & Related papers  (2022-05-23T02:43:45Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)

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.