Phantom of Latent for Large Language and Vision Models (2409.14713v1)

Abstract: The success of visual instruction tuning has accelerated the development of large language and vision models (LLVMs). Following the scaling laws of instruction-tuned LLMs, LLVMs either have further increased their sizes, reaching 26B, 34B, and even 80B parameters. While this increase in model size has yielded significant performance gains, it demands substantially more hardware resources for both training and inference. Consequently, there naturally exists a strong need for efficient LLVMs that achieve the performance of larger models while being smaller in size. To achieve this need, we present a new efficient LLVM family with model sizes of 0.5B, 1.8B, 3.8B, and 7B parameters, Phantom, which significantly enhances learning capabilities within limited structures. By temporarily increasing the latent hidden dimension during multi-head self-attention (MHSA), we make LLVMs prepare to look and understand much more vision-language knowledge on the latent, without substantially increasing physical model sizes. To maximize its advantage, we introduce Phantom Optimization (PO) using both autoregressive supervised fine-tuning (SFT) and direct preference optimization (DPO)-like concept, which effectively follows correct answers while eliminating incorrect and ambiguous ones. Phantom outperforms numerous larger open- and closed-source LLVMs, positioning itself as a leading solution in the landscape of efficient LLVMs.

• The paper introduces Phantom models that enhance LLVM efficiency by temporarily expanding latent dimensions during multi-head self-attention.
• The approach utilizes Phantom Optimization, integrating autoregressive fine-tuning with preference-based training to reduce errors.
• Empirical results show the 7B-parameter Phantom model outperforms larger models on benchmarks like SEED-Bench-2-Plus and MathVista.

Analyzing the Efficiency of Phantom of Latent for Large Language and Vision Models

The paper "Phantom of Latent for Large Language and Vision Models" by Byung-Kwan Lee et al. introduces a novel approach to developing efficient large language and vision models (LLVMs) in an effort to balance high performance with resource constraints. The research context addresses the challenges posed by the substantial hardware requirements for training and inference associated with larger LLVMs, which have surged to sizes like 26B, 34B, and even 80B parameters in pursuit of performance gains.

Approach and Innovation

The core contribution of this research lies in the introduction of a new LLVM family named Phantom, showcasing models at various sizes—0.5B, 1.8B, 3.8B, and 7B parameters. These models aim to significantly enhance learning capabilities within limited structures by increasing the latent hidden dimension temporarily during multi-head self-attention (MHSA) operations. This technique, termed Phantom Dimension, allows the models to incorporate a broader scope of vision-language knowledge without a substantial increase in physical model size.

To amplify the benefits of this approach, the authors introduce Phantom Optimization (PO). Inspired by concepts from Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), PO focuses on producing correct answers while minimizing incorrect and ambiguous ones, thus improving efficiency and overall model performance. This is achieved through two steps of autoregressive supervised fine-tuning and preference-based training.

Numerical Performance

The Phantom models exhibit remarkable performance across a range of benchmarks. Notably, the 7B parameter Phantom model outperforms many larger open- and closed-source LLVMs, establishing itself as a leading solution in the field of efficient LLVMs. Table 1 in the paper indicates Phantom achieving top scores in benchmarks such as SEED-Bench-2-Plus, SEED-IMG, and MathVista, showcasing its enhanced vision-language processing capabilities.

Practical and Theoretical Implications

The practical impact of this research is significant, especially for applications constrained by computational resources, such as mobile and embedded systems. By reducing the resource requirements for training and inference, the Phantom models facilitate the deployment of advanced AI capabilities in real-time applications like augmented reality (AR) systems.

From a theoretical perspective, this paper challenges the prevailing notion that scaling up model parameters and datasets is the sole pathway to improved performance. Instead, the Phantom family of models demonstrates that strategic enhancements in latent feature dimensions and optimization techniques can yield comparable, if not superior, results.

Future Developments in AI

Looking ahead, the methodologies introduced in this paper may inspire further innovations in the efficient modeling of LLVMs. Future research could explore more sophisticated techniques for dynamically adjusting latent dimensions or integrating even finer-grained optimization strategies. Additionally, there might be an increased focus on cross-modal learning and the ability of models to handle multiple types of data inputs more efficiently.

The discussion outlined by the authors acknowledges the potential for deeper explorations into the use of open-source models' textual outputs and parameter sets. As research progresses, the community might see advancements in layer-wise distillation methods, enhancing the transfer of knowledge across diverse architectures, and further optimizing the balance between performance and computational efficiency.

Conclusion

The Phantom LLVM family represents a significant step towards making high-performing vision-LLMs more accessible and resource-efficient. By employing innovative techniques in latent dimension enhancement and optimization, this research offers both practical solutions for immediate deployment and a theoretical foundation for future advancements in the field. As AI continues to evolve, efforts like those described in this paper will likely play a critical role in shaping the landscape of efficient and capable multimodal learning systems.