arXiv:2407.14093v1 Announce Type: new
Abstract: Recently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-off between modal capacity and efficiency of multi-modal large language models (MLLMs). Different from previous efforts, we are dedicated to exploring the dynamic expert path in an already exist MLLM and show that a standard MLLM can be also a mixture of experts. To approach this target, we propose a novel dynamic expert scheme for MLLMs, termed Routing Experts (RoE), which can achieve example-dependent optimal path routing without obvious structure tweaks. Meanwhile, a new regularization of structure sparsity is also introduced to enforce MLLMs to learn more short-cut inference, ensuring the efficiency. In addition, we also realize the first attempt of aligning the training and inference schemes of MLLMs in terms of network routing. To validate RoE, we apply it to a set of latest MLLMs, including LLaVA-1.5, LLaVA-HR and VILA, and conduct extensive experiments on a bunch of VL benchmarks. The experiment results not only show the great advantages of our RoE in improving MLLMs’ efficiency, but also yield obvious advantages than MoE-LLaVA in both performance and speed, e.g., an average performance gain of 3.3% on 5 benchmarks while being faster.
Exploring the Dynamic Expert Path in Multi-Modal Large Language Models
In recent years, the use of multi-modal large language models (MLLMs) has gained popularity in various applications such as natural language processing, computer vision, and information retrieval. These models combine different modalities (e.g., text, images, audio) to achieve better performance. However, one of the challenges in MLLMs is finding the right balance between model capacity and efficiency.
A new approach called mixture of experts (MoE) has emerged as a solution to this challenge. MoE allows for the combination of multiple modalities while efficiently utilizing computational resources. The concept of MoE involves dividing the model into multiple “experts” that specialize in processing specific modalities. These experts then collaborate to make predictions.
In this article, the authors propose a novel approach called Routing Experts (RoE) to further enhance the efficiency of MLLMs. Unlike previous approaches, RoE focuses on dynamically routing examples to the most appropriate expert, without the need for significant modifications to the model structure. This dynamic routing allows for example-dependent optimal path routing, leading to improved performance.
Additionally, the authors introduce a new regularization technique to enforce structure sparsity in MLLMs. This regularization encourages the learning of more efficient inference pathways within the models, further enhancing efficiency. The authors also highlight the significance of aligning the training and inference schemes of MLLMs, ensuring consistency in network routing.
To validate the effectiveness of RoE, the authors conduct extensive experiments on a set of state-of-the-art MLLMs, including LLaVA-1.5, LLaVA-HR, and VILA. These models are evaluated on a range of visual-language benchmarks. The experimental results demonstrate that RoE not only improves the efficiency of MLLMs but also outperforms MoE-LLaVA in terms of both performance and speed. On average, RoE achieves a 3.3% performance gain across five benchmarks while being faster.
This research highlights the multi-disciplinary nature of the concepts involved. The combination of natural language processing, computer vision, and neural networks makes this work relevant to the wider field of multimedia information systems. The concepts of RoE and MoE can also be extended to other areas such as animations, artificial reality, augmented reality, and virtual realities. By optimizing efficiency and performance in MLLMs, these concepts contribute to the development of more powerful and responsive multimedia systems.