AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning

• URL: http://arxiv.org/abs/2301.12132v3
• Date: Mon, 29 Jan 2024 10:41:51 GMT
• Title: AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
• Authors: Han Zhou, Xingchen Wan, Ivan Vuli\'c, Anna Korhonen
• Abstract summary: Motivated by advances in neural architecture search, we propose AutoPEFT for automatic PEFT configuration selection. We show that AutoPEFT-discovered configurations significantly outperform existing PEFT methods and are on par or better than FFT without incurring substantial training efficiency costs.
• Score: 77.61565726647784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong task performance while updating much fewer parameters than full model fine-tuning (FFT). However, it is non-trivial to make informed design choices on the PEFT configurations, such as their architecture, the number of tunable parameters, and even the layers in which the PEFT modules are inserted. Consequently, it is highly likely that the current, manually designed configurations are suboptimal in terms of their performance-efficiency trade-off. Inspired by advances in neural architecture search, we propose AutoPEFT for automatic PEFT configuration selection: we first design an expressive configuration search space with multiple representative PEFT modules as building blocks. Using multi-objective Bayesian optimisation in a low-cost setup, we then discover a Pareto-optimal set of configurations with strong performance-cost trade-offs across different numbers of parameters that are also highly transferable across different tasks. Empirically, on GLUE and SuperGLUE tasks, we show that AutoPEFT-discovered configurations significantly outperform existing PEFT methods and are on par or better than FFT without incurring substantial training efficiency costs.

Related papers

• BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models [63.52035708182815]
  We introduce a novel Budget-guided Iterative search strategy for automatic PEFT (BIPEFT) BIPEFT employs a new iterative search strategy to disentangle the binary module and rank dimension search spaces. Extensive experiments on public benchmarks demonstrate the superior performance of BIPEFT for downstream tasks with a low parameter budget.
  arXiv  Detail & Related papers  (2024-10-04T18:50:46Z)
• Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning [17.032155725171958]
  We propose the Light-PEFT framework, which includes two methods: Masked Early Pruning of the Foundation Model and Multi-Granularity Early Pruning of PEFT. Compared to utilizing the PEFT method directly, Light-PEFT achieves training and inference speedup, reduces memory usage, and maintains comparable performance.
  arXiv  Detail & Related papers  (2024-06-06T07:03:29Z)
• 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)
• Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation [67.13876021157887]
  Dynamic Tuning (DyT) is a novel approach to improve both parameter and inference efficiency for ViT adaptation. DyT achieves superior performance compared to existing PEFT methods while evoking only 71% of their FLOPs on the VTAB-1K benchmark.
  arXiv  Detail & Related papers  (2024-03-18T14:05:52Z)
• Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning [12.648711621637663]
  This paper introduces a novel. COCO-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models. We propose Context-PEFT, which learns different groups of adaptor parameters based on the token's domain. Our method is evaluated on the captioning task, where it outperforms full fine-tuning under similar data constraints.
  arXiv  Detail & Related papers  (2023-12-14T13:00:24Z)
• ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization [100.90624220423634]
  We present ComPEFT, a novel method for compressing fine-tuning residuals (task vectors) of PEFT based models. In extensive evaluation across T5, T0, and LLaMA-based models with 200M - 65B parameters, ComPEFT achieves compression ratios of 8x - 50x.
  arXiv  Detail & Related papers  (2023-11-22T05:28:59Z)
• Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling [42.42235704360381]
  Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks. These large scales make it impossible to adapt and deploy fully specialized models given a task of interest. In this work, we describe AdaLink as a non-intrusive PEFT technique that achieves competitive performance.
  arXiv  Detail & Related papers  (2023-10-18T16:43:08Z)
• DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning [14.975436239088312]
  We propose DePT, which decomposes the soft prompt into a shorter soft prompt and a pair of low-rank matrices that are then optimised with two different learning rates. We demonstrate that DePT outperforms state-of-the-art PEFT approaches, including the full fine-tuning baseline, in some scenarios.
  arXiv  Detail & Related papers  (2023-09-11T00:02:05Z)
• 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)

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.