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.

翻译：软提示调优利用连续嵌入在大型预训练语言模型（LLMs）中捕获任务特定信息，在少样本场景下实现了有竞争力的性能。然而，软提示依赖于高维、隐式的表示，缺乏明确的语义和可追踪的训练行为，这限制了其可解释性。为应对这一局限，我们提出了一种基于拓扑形态演化的软提示调优优化方法。具体而言，我们采用拓扑数据分析（TDA）中的持续同调来量化软提示在连续参数空间中的结构表示及其训练过程的演化。定量分析表明，拓扑结构稳定且紧凑的软提示能获得更好的下游性能。基于这一实证观察，我们构建了一个用于优化软提示调优的损失函数，称为拓扑软提示损失（TSLoss）。TSLoss通过量化参数间的连接性与冗余性，引导模型学习结构稳定的适配。大量实验表明，使用TSLoss进行训练能加速收敛并提升调优性能，为从结构和拓扑角度理解与优化软提示调优提供了一种可解释的方法。