CCoE: A Compact LLM with Collaboration of Experts

• URL: http://arxiv.org/abs/2407.11686v3
• Date: Thu, 25 Jul 2024 03:34:56 GMT
• Title: CCoE: A Compact LLM with Collaboration of Experts
• Authors: Shaomang Huang, Jianfeng Pan, Hanzhong Zheng,
• Abstract summary: We propose a framework of easily coupling strong domain experts together to fuse into a big Large Language Model (LLM) We start with 5 experts in the domain of Code, Law, text-to- Math and Medical. The results indicate that our CCoE framework can easily and efficiently boost nearly 10%-20% performance on original base model in different domains but using less resources on training, as well as inference.
• Score: 0.6144680854063939
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the domain of Large Language Model (LLM), LLMs demonstrate significant capabilities in natural language understanding and generation. With the growing needs of applying LLMs on various domains, it is a research question that how to efficiently train and build a model that has expertise in different domains but with a low training cost. We propose CCoE architecture, a framework of easily coupling multiple strong domain experts together to fuse into a big LLM, provides a collective way of utilizing the different domain expert LLMs. Besides, training a large collaborative of multiple expert LLMs requires a high requirements on training sources. CCoE bypasses this problem through isolating other experts and train each expert separately. The design of CCoE assembles multiple expert LLMs through the CoE (Collaboration of Experts) layer. Each CoE layer could have one or more expert LLMs. Expert LLMs have different number of layers and have been well-trained for different domain tasks. Each expert is fine-tuned to be able to achieve the comparable results with SOTA domain LLMs. We start from 5 experts in the domain of Code, Math, Law, text-to-SQL and Medical. The results indicate that our CCoE framework can easily and efficiently boost nearly 10%-20% performance on original base model in different domains but using less resources on training, as well as inference.

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