From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches

• URL: http://arxiv.org/abs/2503.08998v1
• Date: Wed, 12 Mar 2025 02:17:31 GMT
• Title: From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches
• Authors: Wei Ruan, Tianze Yang, Yifan Zhou, Tianming Liu, Jin Lu,
• Abstract summary: This paper establishes a new taxonomy of model merging methods, systematically comparing different approaches and providing an overview of key developments.<n>Despite the rapid progress in this field, a comprehensive taxonomy and survey summarizing recent advances and predicting future directions are still lacking.
• Score: 13.778158813149833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model merging has achieved significant success, with numerous innovative methods proposed to enhance capabilities by combining multiple models. However, challenges persist due to the lack of a unified framework for classification and systematic comparative analysis, leading to inconsistencies in terminologies and categorizations. Meanwhile, as an increasing number of fine-tuned models are publicly available, their original training data often remain inaccessible due to privacy concerns or intellectual property restrictions. This makes traditional multi-task learning based on shared training data impractical. In scenarios where direct access to training data is infeasible, merging model parameters to create a unified model with broad generalization across multiple domains becomes crucial, further underscoring the importance of model merging techniques. Despite the rapid progress in this field, a comprehensive taxonomy and survey summarizing recent advances and predicting future directions are still lacking. This paper addresses these gaps by establishing a new taxonomy of model merging methods, systematically comparing different approaches, and providing an overview of key developments. By offering a structured perspective on this evolving area, we aim to help newcomers quickly grasp the field's landscape and inspire further innovations.

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