MiCA Learns More Knowledge Than LoRA and Full Fine-Tuning
This addresses the need for parameter-efficient fine-tuning in AI, offering a novel method that is incremental but shows strong gains.
The paper tackles the problem of efficiently fine-tuning large language models by proposing Minor Component Adaptation (MiCA), which adapts underutilized subspaces, resulting in up to 5.9x improvement in knowledge acquisition with a minimal parameter footprint of 6-60% compared to LoRA.
Minor Component Adaptation (MiCA) is a novel parameter-efficient fine-tuning method for large language models that focuses on adapting underutilized subspaces of model representations. Unlike conventional methods such as Low-Rank Adaptation (LoRA), which target dominant subspaces, MiCA leverages Singular Value Decomposition to identify subspaces related to minor singular vectors associated with the least significant singular values and constrains the update of parameters during fine-tuning to those directions. This strategy leads to up to 5.9x improvement in knowledge acquisition under optimized training hyperparameters and a minimal parameter footprint of 6-60% compared to LoRA. These results suggest that constraining adaptation to minor singular directions provides a more efficient and stable mechanism for integrating new knowledge into pre-trained language models.