SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models (2311.07575v1)

Abstract: We present SPHINX, a versatile multi-modal LLM (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-language alignment, we unfreeze the LLM during pre-training, and introduce a weight mix strategy between LLMs trained by real-world and synthetic data. By directly integrating the weights from two domains, the mixed LLM can efficiently incorporate diverse semantics with favorable robustness. Then, to enable multi-purpose capabilities, we mix a variety of tasks for joint visual instruction tuning, and design task-specific instructions to avoid inter-task conflict. In addition to the basic visual question answering, we include more challenging tasks such as region-level understanding, caption grounding, document layout detection, and human pose estimation, contributing to mutual enhancement over different scenarios. Additionally, we propose to extract comprehensive visual embeddings from various network architectures, pre-training paradigms, and information granularity, providing LLMs with more robust image representations. Based on our proposed joint mixing, SPHINX exhibits superior multi-modal understanding capabilities on a wide range of applications. On top of this, we further propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. With a mixing of different scales and high-resolution sub-images, SPHINX attains exceptional visual parsing and reasoning performance on existing evaluation benchmarks. We hope our work may cast a light on the exploration of joint mixing in future MLLM research. Code is released at https://github.com/Alpha-VLLM/LLaMA2-Accessory.

Overview of SPHINX: Advancements in Multi-modal LLMs

The paper introduces SPHINX, a multi-modal LLM (MLLM) engineered to enhance the integration of visual and linguistic modalities for diverse applications. This model distinguishes itself by implementing a joint mixing approach that amalgamates model weights, tuning tasks, and visual embeddings, setting a new benchmark for multi-purpose visual instruction-following capabilities.

Key Contributions

1. Unfreezing and Weight Mixing:

To bolster vision-language alignment, SPHINX diverges from traditional LLMs by unfreezing weights during pre-training. A notable strategy involves weight mixing between models trained on real-world and synthetic data. By linearly combining these domain-specific models, SPHINX adeptly incorporates diverse semantic knowledge while maintaining robustness.

1. Multifaceted Task Integration:

SPHINX's robustness is further elevated by mixing various visual tasks, each with distinct instructions to mitigate inter-task conflicts. Beyond basic visual question answering, SPHINX tackles complex tasks such as region-level understanding and human pose estimation. This extensive task integration fosters mutual capability enhancement across scenarios.

1. Rich Visual Embeddings:

The model capitalizes on embedding extraction from multiple network architectures with varying pre-training paradigms and granularity levels. This comprehensive visual representation is achieved by mixing embeddings from both global and local contexts, enhancing SPHINX's visual parsing abilities.

Practical Implications

SPHINX's design shows promising advancements in multi-modal understanding, facilitating superior performance across diverse application areas. The model's capability to handle high-resolution images through a novel mixing strategy—processing multiple scales and sub-images—addresses a significant constraint present in existing MLLMs.

The proposed approach not only advances visual reasoning and parsing but also lays the groundwork for integrating with other visual foundation models, such as SAM and Stable Diffusion, for broader functional applications like language-referred segmentation and image editing.

Experimental Validation

SPHINX demonstrates impressive results on established benchmarks, outperforming state-of-the-art models in various tasks. The model's performance underscores its effectiveness in adapting to different domains and tasks, suggesting broader applicability in real-world settings.

Theoretical Implications and Future Prospects

Theoretically, SPHINX's joint mixing strategy offers a novel paradigm for synergizing different types of data and tasks within a unified model framework. This innovative approach could inspire future MLLM research to further explore domain-specific fine-tuning and task integration.

Future research may extend SPHINX's capabilities by incorporating additional modalities or expanding on task diversity, thereby pushing the boundaries of AI's interpretive and generative proficiency across modalities.

In summary, SPHINX represents a significant step forward in multi-modal AI integration, combining sophisticated strategies to enhance its overall functionality and adaptability. This paper establishes foundational work that could propel future developments in AI research, particularly within the domain of multi-modal LLMs.