Revolutionizing AI Chat: How FUSECHAT Merges Multiple Language Models into a Superior, Memory-Efficient LLM

The natural language processing (NLP) field has witnessed significant advancements with the emergence of Large Language Models (LLMs) like GPT and LLaMA. These models have become essential tools for various tasks, prompting a growing need for proprietary LLMs among individuals and organizations. However, the resource-intensive nature of LLM development remains a challenge for many. Researchers have proposed knowledge fusion of LLMs as an alternative approach to building powerful models while reducing development costs. This method combines multiple LLMs into a unified framework to leverage their strengths across different tasks.

Previous attempts to integrate multiple models have relied on ensemble methods or direct merging of neural networks. While effective, these approaches often encounter inefficiencies during inference or require uniform network architectures for merging. FUSELLM introduced a novel paradigm for knowledge fusion, utilizing probability distribution matrices generated by multiple source LLMs to transfer collective knowledge into a target LLM through lightweight continual training. This methodology enables the fusion of pre-trained LLMs with diverse architectures into a cohesive model.

Expanding upon the principles of FUSELLM, the study presents FUSECHAT, specifically tailored for fusing chat LLMs with varying architectures and scales. FUSECHAT proceeds in two main stages: knowledge fusion of source LLMs with different structures and scales and merging within the parameter space to incorporate collective knowledge from the source models. The method introduces VARM (Variation Ratio Merge), a novel approach for determining combining weights based on the variation ratio of parameter matrices before and after fine-tuning. This allows for fine-grained merging without additional training efforts.

Empirical evaluation of FUSECHAT using representative open-source chat LLMs demonstrates its effectiveness. Results on MT-Bench, a benchmark assessing multi-turn dialogue ability, indicate that FUSECHAT outperforms individual source LLMs and fine-tuned baselines across different scales. Notably, the proposed VARM merging method achieves superior performance, highlighting the effectiveness of merging weights based on variation ratios. With its scalability and flexibility, FUSECHAT presents a promising solution for integrating chat models amidst the evolving landscape of open-source LLM development.

The development of FUSECHAT represents a significant advancement in the field of multi-model LLM integration, particularly in the realm of chat-based applications. By leveraging knowledge fusion techniques, FUSECHAT offers a practical and efficient approach to combining the capabilities of diverse chat LLMs, addressing the challenges of resource-intensive model development. Its ability to seamlessly integrate models with varying architectures and scales, coupled with the effectiveness of the VARM merging method, positions FUSECHAT as a versatile tool for enhancing dialogue systems’ performance. As the demand for sophisticated chat-based AI systems continues to grow, FUSECHAT is poised to be pivotal in driving innovation and advancements in this domain.

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