Multimodal Large Language Models

Report on Current Developments in Multimodal Large Language Models

General Direction of the Field

The field of Multimodal Large Language Models (MLLMs) is witnessing a significant shift towards enhancing efficiency, scalability, and robustness in handling complex visual-textual data. Recent developments focus on optimizing these models for resource-constrained environments, improving data and compute efficiency, and advancing the integration of visual and textual information at various granularities.

  1. Efficiency and Scalability: There is a strong emphasis on developing models that can operate efficiently in resource-limited settings. Techniques such as token dropping, attention reuse, and hybrid visual encoding are being explored to reduce computational overhead and improve throughput without compromising accuracy.

  2. Data and Compute Efficiency: Researchers are addressing the trade-offs between data efficiency and computational efficiency. Novel architectures and attention mechanisms are being introduced to enhance both aspects simultaneously, ensuring that MLLMs can perform well with fewer data and lower computational resources.

  3. Integration of Visual and Textual Information: The field is moving towards more sophisticated methods of integrating visual and textual data. Approaches like supervised embedding alignment and semantic alignment are being developed to ensure a more coherent and effective fusion of multimodal information, particularly in complex multi-image scenarios.

  4. Cross-Modal Instruction and Understanding: There is an increasing focus on improving the model's ability to handle cross-modal instructions and understand diverse image variations. This includes enhancing the preservation of linking information among images and improving the model's ability to perceive details in visual data.

  5. Democratization of AI: There is a growing trend towards developing models that are accessible and efficient, even on small compute footprints. This includes the development of ternary models and the open-sourcing of training scripts to encourage broader participation and innovation in the field.

Noteworthy Papers

  • HiRED: Introduces a token-dropping scheme that significantly improves throughput and reduces latency and memory usage in resource-constrained environments.
  • EE-MLLM: Achieves both data and compute efficiency by modifying the self-attention mechanism, demonstrating effectiveness across various benchmarks.
  • SEA: Enhances the performance and interpretability of MLLMs through token-level alignment, particularly beneficial for smaller models.
  • TReX: Optimizes Vision Transformers for energy-efficient deployment, achieving significant reductions in energy-delay-area product.
  • MaVEn: Enhances MLLMs' ability to process and interpret information from multiple images, improving performance in complex scenarios.
  • IAA: Empowers frozen Large Language Models with multimodal capabilities without sacrificing NLP performance, outperforming previous methods.
  • SAM: Introduces semantic alignment to preserve linking information among images, significantly improving performance in group captioning and storytelling tasks.
  • ParGo: Bridges the vision-language gap by integrating partial and global views, outperforming conventional projectors in detail perception tasks.
  • LLaVaOLMoBitnet1B: Represents a significant step towards democratizing AI with the first ternary Multimodal LLM capable of handling Image(s)+Text inputs efficiently.

These developments highlight the innovative strides being made in the field of MLLMs, pushing the boundaries of efficiency, integration, and accessibility.

Sources

HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments

EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model

SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs

TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing

MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model

Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese

Building and better understanding vision-language models: insights and future directions

IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities

Semantic Alignment for Multimodal Large Language Models

ParGo: Bridging Vision-Language with Partial and Global Views

LLaVaOLMoBitnet1B: Ternary LLM goes Multimodal!