Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer

• URL: http://arxiv.org/abs/2312.05842v1
• Date: Sun, 10 Dec 2023 09:52:32 GMT
• Title: Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer
• Authors: Yongheng Deng, Ziqing Qiao, Ju Ren, Yang Liu, Yaoxue Zhang
• Abstract summary: Large language models (LLMs) are empowered with broad knowledge, but their task-specific performance is often suboptimal. It necessitates fine-tuning LLMs with task-specific data, but such data may be inaccessible due to privacy concerns. We propose a novel approach to enhance LLMs with smaller language models (SLMs) that are trained on clients using their private task-specific data.
• Score: 27.63746419563747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models (LLMs) are empowered with broad knowledge, their task-specific performance is often suboptimal. It necessitates fine-tuning LLMs with task-specific data, but such data may be inaccessible due to privacy concerns. In this paper, we propose a novel approach to enhance LLMs with smaller language models (SLMs) that are trained on clients using their private task-specific data. To enable mutual enhancement between LLMs and SLMs, we propose CrossLM, where the SLMs promote the LLM to generate task-specific high-quality data, and both the LLM and SLMs are enhanced with the generated data. We evaluate CrossLM using publicly accessible language models across a range of benchmark tasks. The results demonstrate that CrossLM significantly enhances the task-specific performance of SLMs on clients and the LLM on the cloud server simultaneously while preserving the LLM's generalization capability.

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