CBP-Tuning: Efficient Local Customization for Black-box Large Language Models

• URL: http://arxiv.org/abs/2509.12112v1
• Date: Mon, 15 Sep 2025 16:41:08 GMT
• Title: CBP-Tuning: Efficient Local Customization for Black-box Large Language Models
• Authors: Jiaxuan Zhao, Naibin Gu, Yuchen Feng, Xiyu Liu, Peng Fu, Zheng Lin, Weiping Wang,
• Abstract summary: We propose CBP-Tuning, a novel framework that facilitates efficient local customization while preserving bidirectional privacy.<n>Specifically, we design a two-stage framework: (1) a prompt generator trained on the server-side to capture domain-specific and task-agnostic capabilities, and (2) user-side gradient-free optimization that tailors soft prompts for individual tasks.<n>This approach eliminates the need for users to access model weights or upload private data, requiring only a single customized vector per task while achieving effective adaptation.
• Score: 23.249724558362136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The high costs of customizing large language models (LLMs) fundamentally limit their adaptability to user-specific needs. Consequently, LLMs are increasingly offered as cloud-based services, a paradigm that introduces critical limitations: providers struggle to support personalized customization at scale, while users face privacy risks when exposing sensitive data. To address this dual challenge, we propose Customized Black-box Prompt Tuning (CBP-Tuning), a novel framework that facilitates efficient local customization while preserving bidirectional privacy. Specifically, we design a two-stage framework: (1) a prompt generator trained on the server-side to capture domain-specific and task-agnostic capabilities, and (2) user-side gradient-free optimization that tailors soft prompts for individual tasks. This approach eliminates the need for users to access model weights or upload private data, requiring only a single customized vector per task while achieving effective adaptation. Furthermore, the evaluation of CBP-Tuning in the commonsense reasoning, medical and financial domain settings demonstrates superior performance compared to baselines, showcasing its advantages in task-agnostic processing and privacy preservation.

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