Training-free LLM Merging for Multi-task Learning
- Publisher:
- Association for Computational Linguistics (ACL)
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2025, 1, pp. 33111-33124
- Issue Date:
- 2025-01-01
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Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen has triggered the development of numerous fine-tuned models tailored for various tasks and languages. In this paper, we explore an important question: is it possible to combine these specialized models to create a unified model with multi-task capabilities. We introduces Hierarchical Iterative Merging (Hi-Merging), a training-free method for unifying different specialized LLMs into a single model. Specifically, Hi-Merging employs model-wise and layer-wise pruning and scaling, guided by contribution analysis, to mitigate parameter conflicts. Extensive experiments on multiple-choice and question-answering tasks in both Chinese and English validate Hi-Merging's ability for multi-task learning. The results demonstrate that Hi-Merging consistently outperforms existing merging techniques and surpasses the performance of models fine-tuned on combined datasets in most scenarios. Code is available at Applied-Machine-Learning-Lab/Hi-Merging.
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