WaveletGPT: Leveraging Wavelet Theory for Speedier LLM Training Across Modalities

Practical Solutions and Value of WaveletGPT for AI Evolution WaveletGPT integrates wavelets into Large Language Models to enhance performance without extra parameters, speeding up training by 40-60% in different areas. Wavelet-Based Intermediate Operation By incorporating wavelet transforms, WaveletGPT provides access to multi-resolution representations at each layer, significantly improving model performance. Improved Training Efficiency WaveletGPT accelerates pre-training of transformer-based models without added complexity, delivering performance gains similar to increasing layers or parameters, streamlining AI development. Multi-Modal Performance Enhancements Wavelet-based operations offer performance boosts across language, audio, and music datasets, showcasing flexibility. Learnable wavelet kernels further strengthen model capabilities. Key Implementation Steps 1. Integrate wavelets into LLM architecture. 2. Utilize discrete wavelet transform for multi-scale filters. 3. Implement Haar wavelets for structured data representation. 4. Maintain causality assumption for accurate next-token prediction. 5. Enhance model performance while simplifying architecture. Future AI Optimization Explore advanced wavelet concepts to further optimize large language models. WaveletGPT sets the stage for utilizing wavelet theory in AI advancement across various industries.