Higher-order interactions of multi-layer prompt

• URL: http://arxiv.org/abs/2510.09394v2
• Date: Thu, 16 Oct 2025 15:48:27 GMT
• Title: Higher-order interactions of multi-layer prompt
• Authors: Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Xinyan Huang, Weigang Lu,
• Abstract summary: "Pre-train, prompt" paradigm has successfully evolved in representation learning.<n>Current prompt-tuning methods treat prompts as isolated, independent components across different network layers.<n>We propose a novel framework that explicitly models the Higher-order Interactions of Multi-layer Prompt.
• Score: 22.205298192935313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The "pre-train, prompt" paradigm has successfully evolved in representation learning. While current prompt-tuning methods often introduce learnable prompts, they predominantly treat prompts as isolated, independent components across different network layers. This overlooks the complex and synergistic higher-order interactions that exist between prompts at various hierarchical depths, consequently limiting the expressive power and semantic richness of the prompted model. To address this fundamental gap, we propose a novel framework that explicitly models the Higher-order Interactions of Multi-layer Prompt. Our approach conceptualizes prompts from different layers not as separate entities, but as a cohesive system where their inter-relationships are critical. We design an innovative interaction module that captures these sophisticated, non-linear correlations among multi-layer prompts, effectively modeling their cooperative effects. This allows the model to dynamically aggregate and refine prompt information across the network's depth, leading to a more integrated and powerful prompting strategy. Extensive experiments on eight benchmark datasets demonstrate that our method, by leveraging these higher-order interactions, consistently surpasses state-of-the-art prompt-tuning baselines. The performance advantage is particularly pronounced in few-shot scenarios, validating that capturing the intricate interplay between multi-layer prompts is key to unlocking more robust and generalizable representation learning.

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