Optimizing Soft Prompt Tuning via Structural Evolution
🤖 AI Summary
This work addresses the limited interpretability of soft prompt tuning, which stems from the absence of explicit semantic structure and traceable training dynamics. To this end, we introduce topological data analysis into soft prompt optimization for the first time, proposing a Topological Structure Loss (TSLoss) based on persistent homology. TSLoss quantifies the topological structure of soft prompts in parameter space and its evolution throughout training, thereby guiding the model to learn representations that are both structurally stable and compact. Our approach not only enhances the interpretability of the tuning process but also significantly accelerates convergence and improves downstream task performance, demonstrating the critical role of topological stability in the effectiveness of soft prompts.
Application Category
📝 Abstract
Soft prompt tuning leverages continuous embeddings to capture task-specific information in large pre-trained language models (LLMs), achieving competitive performance in few-shot settings. However, soft prompts rely on high-dimensional, implicit representations and lack explicit semantics and traceable training behaviors, which limits their interpretability. To address this limitation, we propose a soft prompt tuning optimization method based on topological morphological evolution. Specifically, we employ persistent homology from topological data analysis (TDA) to quantify the structural representations of soft prompts in continuous parameter space and their training process evolution. Quantitative analysis shows that topologically stable and compact soft prompts achieve better downstream performance. Based on this empirical observation, we construct a loss function for optimizing soft prompt tuning, termed Topological Soft Prompt Loss (TSLoss). TSLoss guides the model to learn structurally stable adaptations by quantifying inter-parameter connectivity and redundancy. Extensive experiments show that training with TSLoss accelerates convergence and improves tuning performance, providing an interpretable method to understand and optimize soft prompt tuning from structural and topological perspectives.
Problem
Research questions and friction points this paper is trying to address.
soft prompt tuning
interpretability
topological data analysis
structural representation
large language models
Innovation
Methods, ideas, or system contributions that make the work stand out.
soft prompt tuning
topological data analysis
persistent homology
structural evolution
TSLoss
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