PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models

• URL: http://arxiv.org/abs/2406.12403v1
• Date: Tue, 18 Jun 2024 08:48:14 GMT
• Title: PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models
• Authors: Tao Fan, Yan Kang, Weijing Chen, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang,
• Abstract summary: PDSS works on a server-client architecture, wherein client transmits prompts to the server's LLM for rationale generation. The generated rationales are then decoded by the client and used to enrich the training of task-specific small language model. Experiments demonstrate the effectiveness of PDSS in various text generation tasks, enabling the training of task-specific SLM with enhanced performance.
• Score: 29.58928014528991
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the context of real-world applications, leveraging large language models (LLMs) for domain-specific tasks often faces two major challenges: domain-specific knowledge privacy and constrained resources. To address these issues, we propose PDSS, a privacy-preserving framework for step-by-step distillation of LLMs. PDSS works on a server-client architecture, wherein client transmits perturbed prompts to the server's LLM for rationale generation. The generated rationales are then decoded by the client and used to enrich the training of task-specific small language model(SLM) within a multi-task learning paradigm. PDSS introduces two privacy protection strategies: the Exponential Mechanism Strategy and the Encoder-Decoder Strategy, balancing prompt privacy and rationale usability. Experiments demonstrate the effectiveness of PDSS in various text generation tasks, enabling the training of task-specific SLM with enhanced performance while prioritizing data privacy protection.

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