Papers with LLM finetuning approach

1 papers
MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning (2025.naacl-long)

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Challenge: Efficient finetuning of large language models (LLMs) aims to adapt the LLMs with reduced computational and memory costs.
Approach: They propose a simple yet effective method that initializes low-rank matrices with Gaussian distribution and zero values while keeping the original weight matrics frozen.
Outcome: The proposed approach only updates the minor components of the weight matrix while keeping the principal singular components frozen.

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