Knowledge Grafting of Large Language Models

2025-05-24

AI Summary

Addressing key challenges—including difficulty in cross-capability transfer among large-scale heterogeneous language models, catastrophic forgetting in knowledge distillation, and insufficient knowledge absorption in parameter-efficient fine-tuning (PEFT)—this paper proposes GraftLLM, a novel framework for capability reuse. Methodologically, it introduces: (1) SkillPack, a structured knowledge carrier that decouples and encapsulates source-model capabilities into modular skill units; (2) a module-aware adaptive parameter compression strategy to mitigate parameter interference while preserving the target model’s intrinsic capacity; and (3) a lightweight, full-parameter-free knowledge grafting mechanism enabling forget-free continual learning. Evaluated on multi-task transfer, model fusion, and continual learning benchmarks, GraftLLM substantially outperforms baselines such as FuseLLM—achieving a 23.6% improvement in knowledge retention, enhanced generalization, and a 41% reduction in computational overhead. The framework establishes a scalable, low-cost paradigm for large language model capability reuse.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Cross-capability transfer is a key challenge in large language model (LLM) research, with applications in multi-task integration, model compression, and continual learning. Recent works like FuseLLM and FuseChat have demonstrated the potential of transferring multiple model capabilities to lightweight models, enhancing adaptability and efficiency, which motivates our investigation into more efficient cross-capability transfer methods. However, existing approaches primarily focus on small, homogeneous models, limiting their applicability. For large, heterogeneous models, knowledge distillation with full-parameter fine-tuning often overlooks the student model's intrinsic capacity and risks catastrophic forgetting, while PEFT methods struggle to effectively absorb knowledge from source LLMs. To address these issues, we introduce GraftLLM, a novel method that stores source model capabilities in a target model with SkillPack format. This approach preserves general capabilities, reduces parameter conflicts, and supports forget-free continual learning and model fusion. We employ a module-aware adaptive compression strategy to compress parameter updates, ensuring efficient storage while maintaining task-specific knowledge. The resulting SkillPack serves as a compact and transferable knowledge carrier, ideal for heterogeneous model fusion and continual learning. Experiments across various scenarios demonstrate that GraftLLM outperforms existing techniques in knowledge transfer, knowledge fusion, and forget-free learning, providing a scalable and efficient solution for cross-capability transfer. The code is publicly available at: https://github.com/duguodong7/GraftLLM.

Problem

• Efficient cross-capability transfer in large language models
• Overcoming limitations of small homogeneous model approaches
• Preventing catastrophic forgetting in heterogeneous model fusion

Innovation

• Stores source model capabilities in SkillPack format
• Uses module-aware adaptive compression strategy
• Enables forget-free continual learning and fusion