从参数到行为：大型语言模型融合综述
From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
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中文总结 AI 辅助
本文综述了大型语言模型融合领域，将其划分为参数级、表示级和行为级三个层次，并回顾了相关指标、基准与应用，旨在为该领域提供系统性图谱并指引未来研究方向。
中文摘要 AI 辅助
模型融合将源模型的能力整合到单个目标模型中。截至2026年6月，Hugging Face上托管了超过200万个模型。这一不断增长的模型库为模型复用和能力整合提供了丰富的基础。然而，现有综述往往仅覆盖该领域的部分内容，且未提供统一定义或系统性分类。本综述定义了模型融合，并将先前工作组织为三个层次：参数级融合、表示级融合和行为级融合。我们还回顾了相关指标、基准和应用程序，总结了当前挑战，并指出了未来方向。我们的目标是提供该领域的清晰图谱，并支持未来关于模型融合的研究工作。关于模型融合的论文综合列表可在以下网址获取：此https URL。
英文摘要
Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This growing pool provides a rich base for model reuse and capability integration. Yet existing surveys often cover only separate parts of this space, and they do not provide a unified definition or a systematic taxonomy. This survey defines model fusion and organizes prior work into three levels: parameter-level, representation-level, and behavior-level fusion. We also review related metrics, benchmarks, and applications, summarize current challenges, and identify future directions. Our goal is to provide a clear map of this area and support future work on model fusion. A comprehensive list of papers about model fusion is available at https://github.com/Baicaihaochi/Awesome-Model-Fusion-Survey.
发表机构
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- The Hong Kong Polytechnic University（香港理工大学）
- PolyU-Daya Bay Technology and Innovation Research Institute（香港理工大学大亚湾技术创新研究院）
- The Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。
