InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion

• URL: http://arxiv.org/abs/2501.02795v2
• Date: Thu, 09 Jan 2025 04:50:16 GMT
• Title: InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion
• Authors: Zhaoyi Yan, Zhijie Sang, Yiming Zhang, Yuhao Fu, Baoyi He, Qi Zhou, Yining Di, Chunlin Ji, Shengyu Zhang, Fei Wu, Hongxia Yang,
• Abstract summary: This paper explores strategies to integrate multiple domain-specialized models into an efficient pivot model.<n>We propose two fusion strategies to combine the strengths of multiple LLMs.<n>We achieve accuracy improvements of 9.27%, 8.80%, and 8.89% on the GSM8K, MATH, and HumanEval tasks, respectively.
• Score: 35.56060538535215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong performance across various reasoning tasks, yet building a single model that consistently excels across all domains remains challenging. This paper addresses this problem by exploring strategies to integrate multiple domain-specialized models into an efficient pivot model.We propose two fusion strategies to combine the strengths of multiple LLMs: (1) a pairwise, multi-step fusion approach that sequentially distills each source model into the pivot model, followed by a weight merging step to integrate the distilled models into the final model. This method achieves strong performance but requires substantial training effort; and (2) a unified fusion approach that aggregates all source models' outputs simultaneously.To improve the fusion process, we introduce a novel Rate-Skewness Adaptive Fusion (RSAF) technique, which dynamically adjusts top-K ratios during parameter merging for enhanced flexibility and stability.Furthermore, we propose an uncertainty-based weighting method for the unified approach, which dynamically balances the contributions of source models and outperforms other logits/distribution ensemble methods.We achieved accuracy improvements of 9.27%, 8.80%, and 8.89% on the GSM8K, MATH, and HumanEval tasks, respectively.

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