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This is a Quantitative Study study (also classified as: Experimental Study).

October 20, 20250 citationsOpen Access

LoRA-Null: Enhancing Fine-Tuning of Large Language Models via Null Space Initialization

LoRA-Null: Low-Rank Adaptation via Null Space for Large Language Models

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LoRA-Null effectively retains pre-trained knowledge in large language models, highlighting improved fine-tuning outcomes.

LoRA-Null maintains strong fine-tuning performance while preserving pre-trained world knowledge.
Experimental results across tasks show significant improvement when initializing with the null space projection.
Freezing down-projection matrix values during fine-tuning enhances preservation of pre-trained knowledge.
The approach is validated by extensive experiments on the LLaMA series for various tasks.

Tang et al. (2025) studied this question.

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October 20, 20250 citationsOpen Access

LoRA-Null: Low-Rank Adaptation via Null Space for Large Language Models

View Full Paper

Ask AI

Bookmark

Authors

LoRA-Null effectively retains pre-trained knowledge in large language models, highlighting improved fine-tuning outcomes.

LoRA-Null maintains strong fine-tuning performance while preserving pre-trained world knowledge.
Experimental results across tasks show significant improvement when initializing with the null space projection.
Freezing down-projection matrix values during fine-tuning enhances preservation of pre-trained knowledge.
The approach is validated by extensive experiments on the LLaMA series for various tasks.

Tang et al. (2025) studied this question.

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