PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment

• URL: http://arxiv.org/abs/2502.01033v1
• Date: Mon, 03 Feb 2025 04:06:03 GMT
• Title: PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment
• Authors: Zequan Liu, Yi Zhao, Ming Tan, Wei Zhu, Aaron Xuxiang Tian,
• Abstract summary: This paper introduces a new and straightforward PEFT technique, termed underlinePrompt underlineAware underlineRepresentation underlineAdjustment (PARA)
• Score: 11.34564365247007
• License:
• Abstract: In the realm of parameter-efficient fine-tuning (PEFT) methods, while options like LoRA are available, there is a persistent demand in the industry for a PEFT approach that excels in both efficiency and performance within the context of single-backbone multi-tenant applications. This paper introduces a new and straightforward PEFT technique, termed \underline{P}rompt \underline{A}ware \underline{R}epresentation \underline{A}djustment (PARA). The core of our proposal is to integrate a lightweight vector generator within each Transformer layer. This generator produces vectors that are responsive to input prompts, thereby adjusting the hidden representations accordingly. Our extensive experimentation across diverse tasks has yielded promising results. Firstly, the PARA method has been shown to surpass current PEFT benchmarks in terms of performance, despite having a similar number of adjustable parameters. Secondly, it has proven to be more efficient than LoRA in the single-backbone multi-tenant scenario, highlighting its significant potential for industrial adoption.

Related papers

• 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)
• Preserving Pre-trained Representation Space: On Effectiveness of Prefix-tuning for Large Multi-modal Models [24.62337386603331]
  Large Multi-modal Models (LMMs) are revolutionizing the way machines interact with the world. To adapt LMMs for downstream tasks, parameter-efficient fine-tuning (PEFT) has gained popularity. This paper focuses on the strengths and weaknesses of each tuning strategy, shifting the focus from the efficiency typically associated with these approaches.
  arXiv  Detail & Related papers  (2024-10-29T07:55:50Z)
• 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)
• PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification [8.312232079766076]
  We introduce a new and straightforward PEFT methodology named underlinePrompt DunderlineEpenunderlineDent underlineRepresentation MunderlineOdification (PEDRO)
  arXiv  Detail & Related papers  (2024-09-26T13:36:00Z)
• 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)
• IAPT: Instruction-Aware Prompt Tuning for Large Language Models [19.408462115679914]
  We propose a novel prompt tuning method, Instruction-Aware Prompt Tuning (IAPT), that requires only four soft tokens. First, we install a parameter-efficient soft prompt generator at each Transformer layer to generate idiosyncratic soft prompts for each input instruction. Second, the soft prompt generators are modules with a bottleneck architecture consisting of a self-attention pooling operation, two linear projections, and an activation function.
  arXiv  Detail & Related papers  (2024-05-28T14:11:01Z)
• 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)
• Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model [81.55141188169621]
  We equip PEFT with a cross-block orchestration mechanism to enable the adaptation of the Segment Anything Model (SAM) to various downstream scenarios. We propose an intra-block enhancement module, which introduces a linear projection head whose weights are generated from a hyper-complex layer. Our proposed approach consistently improves the segmentation performance significantly on novel scenarios with only around 1K additional parameters.
  arXiv  Detail & Related papers  (2023-11-28T11:23:34Z)
• Parameter-Efficient Fine-Tuning without Introducing New Latency [7.631596468553607]
  We introduce a novel adapter technique that directly applies the adapter to pre-trained parameters instead of the hidden representation. Our proposed method attains a new state-of-the-art outcome in terms of both performance and storage efficiency, storing only 0.03% parameters of full fine-tuning.
  arXiv  Detail & Related papers  (2023-05-26T08:44:42Z)
• 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.