DreamLLM: Synergistic Multimodal Comprehension and Creation (2309.11499v2)

Abstract: This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal LLMs (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy. Project page: https://dreamLLM.github.io.

• The paper introduces a novel framework that samples directly from raw multimodal data, significantly reducing information loss in zero-shot tasks.
• It employs interleaved generative pre-training with a unique <dream> token to effectively encode and decode mixed image-text inputs.
• Experimental results include an 8.46 FID on MS-COCO and strong performance on benchmarks like MMBench and MM-Vet, demonstrating enhanced multimodal capabilities.

Overview of DreamLLM: Synergistic Multimodal Comprehension and Creation

The paper introduces DreamLLM, a learning framework designed to enhance Multimodal LLMs (MLLMs) through a novel integration of multimodal comprehension and creation capabilities. This research addresses the limitations and potential information loss associated with traditional external feature extractors by focusing on raw multimodal space, thereby achieving improved accuracy and understanding across various tasks.

Core Contributions

DreamLLM operates on two essential principles:

1. Generative Modeling in Raw Multimodal Space: The model bypasses common feature extractors such as CLIP by sampling directly from raw multimodal data, extending its comprehension and generative capabilities. This methodology is shown to mitigate information loss, resulting in superior performance in zero-shot settings.
2. Interleaved Generative Pre-Training: Employing a new token, the <dream> token, this approach allows DreamLLM to encode and decode interleaved image-text inputs, learning all conditional, marginal, and joint multimodal distributions. This enables the model to generate free-form interleaved documents effectively, establishing a comprehensive understanding grounded in both creation and comprehension.

Numerical Results

DreamLLM demonstrates enhanced performance across several evaluation benchmarks. Specifically:

• Achieved 8.46 FID on MS-COCO, indicating a significant improvement in image generation quality compared to other MLLMs.
• Excelled in comprehensive benchmarks such as MMBench and MM-Vet, showing superior capabilities in complex multimodal tasks.

These results emphasize DreamLLM's effectiveness as a zero-shot multimodal generalist, making it a notable advancement in the field.

Implications and Future Directions

The implications of DreamLLM are multifaceted:

• Practical Applications: The ability to generate free-form interleaved content opens new possibilities for content creation tools, media generation, and interactive AI systems.
• Theoretical Insights: The paper underlines the significance of leveraging direct raw data interactions over intermediate representation alignment, providing a fresh perspective on optimizing MLLM architectures.
• Future Developments: Potential future directions include expanding the framework to more advanced model sizes and exploring new modalities beyond visual and language inputs, potentially incorporating audio or tactile data.

DreamLLM sets a foundational precedence by successfully synergizing creation with comprehension within MLLMs, suggesting a promising pathway for robust, multimodal AI systems that can more accurately understand and generate complex real-world data. This contribution underlines the capability of MLLMs to not only interpret but also creatively manipulate multimodal inputs, broadening the horizons for future research in AI-driven comprehension and synthesis.