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Knowledge Fusion of Large Language Models Via Modular SkillPacks
arXiv – CS AI|Guodong Du, Zhuo Li, Xuanning Zhou, Junlin Li, Zesheng Shi, Wanyu Lin, Ho-Kin Tang, Xiucheng Li, Fangming Liu, Wenya Wang, Min Zhang, Jing Li||6 views
🤖AI Summary
Researchers introduce GraftLLM, a new method for transferring knowledge between large language models using 'SkillPack' format that preserves capabilities while avoiding catastrophic forgetting. The approach enables efficient model fusion and continual learning for heterogeneous models through modular knowledge storage.
Key Takeaways
- →GraftLLM stores source model capabilities in SkillPack format to enable efficient knowledge transfer between heterogeneous large language models.
- →The method addresses limitations of existing approaches that focus primarily on small, homogeneous models.
- →GraftLLM preserves general capabilities while reducing parameter conflicts and supporting forget-free continual learning.
- →Module-aware adaptive compression strategy ensures efficient storage while maintaining task-specific knowledge.
- →Experiments demonstrate superior performance over existing techniques in knowledge transfer, fusion, and continual learning scenarios.
#large-language-models#knowledge-transfer#model-fusion#continual-learning#ai-research#parameter-efficiency#graftllm#skillpack#catastrophic-forgetting#heterogeneous-models
Read Original →via arXiv – CS AI
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