Efficient Fine-tuning with Fourier-Activated Adapters
Published:Dec 26, 2025 20:50
•1 min read
•ArXiv
Analysis
This paper introduces a novel parameter-efficient fine-tuning method called Fourier-Activated Adapter (FAA) for large language models. The core idea is to use Fourier features within adapter modules to decompose and modulate frequency components of intermediate representations. This allows for selective emphasis on informative frequency bands during adaptation, leading to improved performance with low computational overhead. The paper's significance lies in its potential to improve the efficiency and effectiveness of fine-tuning large language models, a critical area of research.
Key Takeaways
- •Proposes a novel parameter-efficient fine-tuning method called Fourier-Activated Adapter (FAA).
- •FAA uses Fourier features to decompose and modulate frequency components of intermediate representations.
- •Achieves competitive or superior performance compared to existing methods with low overhead.
- •Demonstrates the effectiveness of frequency-aware activation and adaptive weighting.
Reference
“FAA consistently achieves competitive or superior performance compared to existing parameter-efficient fine-tuning methods, while maintaining low computational and memory overhead.”