Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866913866485596160 |
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| author | Kong, Zhenglun Zhan, Zheng Hou, Shiyue Gong, Yifan Meng, Xin Sui, Pengwei Dong, Peiyan Shen, Xuan Wang, Zifeng Zhao, Pu Tang, Hao Ioannidis, Stratis Wang, Yanzhi |
| author_facet | Kong, Zhenglun Zhan, Zheng Hou, Shiyue Gong, Yifan Meng, Xin Sui, Pengwei Dong, Peiyan Shen, Xuan Wang, Zifeng Zhao, Pu Tang, Hao Ioannidis, Stratis Wang, Yanzhi |
| contents | Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities from other specialized LLMs. Popular methods like ensemble and weight merging require substantial memory and struggle to adapt to changing data environments. Recent efforts have transferred knowledge from multiple LLMs into a single target model; however, they suffer from interference and degraded performance among tasks, largely due to limited flexibility in candidate selection and training pipelines. To address these issues, we propose a framework that adaptively selects and aggregates knowledge from diverse LLMs to build a single, stronger model, avoiding the high memory overhead of ensemble and inflexible weight merging. Specifically, we design an adaptive selection network that identifies the most relevant source LLMs based on their scores, thereby reducing knowledge interference. We further propose a dynamic weighted fusion strategy that accounts for the inherent strengths of candidate LLMs, along with a feedback-driven loss function that prevents the selector from converging on a single subset of sources. Experimental results demonstrate that our method can enable a more stable and scalable knowledge aggregation process while reducing knowledge interference by up to 50% compared to existing approaches. Code is avaliable at https://github.com/ZLKong/LLM_Integration |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23844 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation Kong, Zhenglun Zhan, Zheng Hou, Shiyue Gong, Yifan Meng, Xin Sui, Pengwei Dong, Peiyan Shen, Xuan Wang, Zifeng Zhao, Pu Tang, Hao Ioannidis, Stratis Wang, Yanzhi Computation and Language Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities from other specialized LLMs. Popular methods like ensemble and weight merging require substantial memory and struggle to adapt to changing data environments. Recent efforts have transferred knowledge from multiple LLMs into a single target model; however, they suffer from interference and degraded performance among tasks, largely due to limited flexibility in candidate selection and training pipelines. To address these issues, we propose a framework that adaptively selects and aggregates knowledge from diverse LLMs to build a single, stronger model, avoiding the high memory overhead of ensemble and inflexible weight merging. Specifically, we design an adaptive selection network that identifies the most relevant source LLMs based on their scores, thereby reducing knowledge interference. We further propose a dynamic weighted fusion strategy that accounts for the inherent strengths of candidate LLMs, along with a feedback-driven loss function that prevents the selector from converging on a single subset of sources. Experimental results demonstrate that our method can enable a more stable and scalable knowledge aggregation process while reducing knowledge interference by up to 50% compared to existing approaches. Code is avaliable at https://github.com/ZLKong/LLM_Integration |
| title | Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.23844 |