Is Scaling the Only Path to AI Supremacy? This AI Paper Unveils ‘Phantom of Latent for Large Language and Vision Models

Large language and vision models (LLVMs) often struggle with balancing performance improvements and computational efficiency. However, innovative solutions have been developed to address this challenge. One such solution is the introduction of **Phantom Dimension**, which temporarily increases latent hidden dimension during multi-head self-attention (MHSA). This allows for embedding more vision-language knowledge without permanently increasing model size. Another solution is **Phantom Optimization (PO)**, which combines autoregressive supervised fine-tuning (SFT) with direct preference optimization (DPO). This approach enhances efficiency while maintaining high performance levels. The key values of these solutions are: - **Efficiency**: Smaller models can now perform on par with larger models without adding to the computational burden. - **Practicality**: These solutions are suitable for real-time applications and resource-limited environments. - **Performance**: The models equipped with these innovations outperform larger models in tasks such as image understanding, chart interpretation, and mathematical reasoning. In conclusion, the Phantom LLVM family offers practical and efficient solutions to enhance large vision-language models, making them deployable in various scenarios. For more information, refer to the Paper and GitHub resources provided.