MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper

• URL: http://arxiv.org/abs/2509.00996v2
• Date: Sat, 13 Sep 2025 23:37:47 GMT
• Title: MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper
• Authors: Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng, Sohail Dianat, Xiaotian Han, Raghuveer Rao, Xueling Zhang, Cheng Han, Lifu Huang, Dongfang Liu,
• Abstract summary: We propose Mixture of Expert Prompt Tuning (MEPT) as an effective and efficient manifold-mapping framework.<n>MEPT integrates multiple prompt experts to adaptively learn diverse and non-stationary data distributions.<n> Empirical evaluations demonstrate that MEPT outperforms several state-of-the-art parameter efficient baselines on SuperGLUE.
• Score: 75.6582687942241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, and fine-tune adjusts the model parameters to activate specific neural pathways to align with the target manifold. Although prior fine-tuning approaches demonstrate success, their rigid parameter space limits their ability to dynamically activate appropriate neural pathways, rendering them ill-equipped to adapt flexibly to the diverse and evolving data distributions. In light of this view, we propose a novel approach, Mixture of Expert Prompt Tuning (MEPT), as an effective and efficient manifold-mapping framework. MEPT leverages the Mixture of Experts architecture by integrating multiple prompt experts to adaptively learn diverse and non-stationary data distributions. Empirical evaluations demonstrate that MEPT outperforms several state-of-the-art parameter efficient baselines on SuperGLUE, achieving notable improvements in mean accuracy (e.g., 1.94%) while significantly reducing activated prompts by 79.25%. The effectiveness of MEPT is further supported by theoretical insights from manifold learning and validated through neural activation pathway visualization results. Our code is avaliable at https://runjia.tech/emnlp_mept/.

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