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 turn into essential tools for various tasks, prompting a growing need for proprietary LLMs amongst individuals and organizations. Nonetheless, the resource-intensive nature of LLM development stays a challenge for a lot of. Researchers have proposed knowledge fusion of LLMs in its place approach to constructing powerful models while reducing development costs. This method combines multiple LLMs right 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 right into a goal LLM through lightweight continual training. This system enables the fusion of pre-trained LLMs with diverse architectures right into a cohesive model.

Expanding upon the principles of FUSELLM, the study presents FUSECHAT, specifically tailored for fusing chat LLMs with various architectures and scales. FUSECHAT proceeds in two fundamental stages: knowledge fusion of source LLMs with different structures and scales and merging throughout the parameter space to include collective knowledge from the source models. The strategy 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 permits 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 adaptability, FUSECHAT presents a promising solution for integrating chat models amidst the evolving landscape of open-source LLM development.

The event of FUSECHAT represents a big advancement in the sector of multi-model LLM integration, particularly within 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 various architectures and scales, coupled with the effectiveness of the VARM merging method, positions FUSECHAT as a flexible tool for enhancing dialogue systems’ performance. Because the demand for stylish chat-based AI systems continues to grow, FUSECHAT is poised to be pivotal in driving innovation and advancements on this domain.

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