WorldGPT: Empowering LLM as Multimodal World Model (2404.18202v2)

Abstract: World models are progressively being employed across diverse fields, extending from basic environment simulation to complex scenario construction. However, existing models are mainly trained on domain-specific states and actions, and confined to single-modality state representations. In this paper, We introduce WorldGPT, a generalist world model built upon Multimodal LLM (MLLM). WorldGPT acquires an understanding of world dynamics through analyzing millions of videos across various domains. To further enhance WorldGPT's capability in specialized scenarios and long-term tasks, we have integrated it with a novel cognitive architecture that combines memory offloading, knowledge retrieval, and context reflection. As for evaluation, we build WorldNet, a multimodal state transition prediction benchmark encompassing varied real-life scenarios. Conducting evaluations on WorldNet directly demonstrates WorldGPT's capability to accurately model state transition patterns, affirming its effectiveness in understanding and predicting the dynamics of complex scenarios. We further explore WorldGPT's emerging potential in serving as a world simulator, helping multimodal agents generalize to unfamiliar domains through efficiently synthesising multimodal instruction instances which are proved to be as reliable as authentic data for fine-tuning purposes. The project is available on \url{https://github.com/DCDmllm/WorldGPT}.

• The paper introduces WorldGPT, a multimodal large language model functioning as a generalist world model, which predicts state transitions by integrating video knowledge and cognitive architecture for enhanced generalizability.
• The authors created the WorldNet multimodal dataset for training and evaluation, showing WorldGPT's superior performance on the WorldNet-Crafted benchmark for state prediction tasks compared to existing models.
• WorldGPT can be used as a world simulator to generate diverse multimodal instructional data, demonstrating its utility in training agents with synthesized "dream tuning" data comparable to real-world data.

Overview of "WorldGPT: Empowering LLM as Multimodal World Model"

The paper "WorldGPT: Empowering LLM as Multimodal World Model" presents a comprehensive approach to advancing the capabilities of LLMs by integrating them into a multimodal framework suitable for modeling world dynamics. The authors introduce WorldGPT, a generalist world model designed to transcend traditional limitations associated with domain-specific and unimodal state representations.

Key Contributions

1. Development of WorldGPT: At the core of this research is the development of WorldGPT, a multimodal LLM (MLLM) that processes inputs and generates outputs across various modalities. WorldGPT leverages latent knowledge from millions of videos and integrates this with the predictive capabilities of LLMs. This innovative architecture aims to establish a robust world model that can predict any-to-any state transitions.
2. Cognitive Architecture Integration: To enhance generalizability and predictive consistency in complex scenarios, the authors have designed a cognitive architecture encompassing memory offloading, knowledge retrieval, and a component termed ContextReflector. This architecture enables WorldGPT to draw upon external knowledge and past predictions efficiently.
3. WorldNet Dataset: The paper describes the creation of WorldNet, a substantial multimodal dataset partitioned into WorldNet-Wild and WorldNet-Crafted. This dataset serves both as a training resource and as a benchmark for evaluating multimodal world models.
4. Novel Learning Paradigm: The implementation of a progressive training methodology facilitates robust learning of state transitions, further augmented by cognitive tuning to refine performance in unfamiliar domains.
5. Application as a World Simulator: An interesting aspect of this work is using WorldGPT as a world simulator capable of generating diverse multimodal instructional data, enhancing the learning of multimodal agents through what the authors term "dream tuning."

Numerical Results and Evaluation

The paper provides a detailed evaluation of WorldGPT using the WorldNet-Crafted benchmark. WorldGPT demonstrated superior performance across a range of unimodal and multimodal state prediction tasks when compared to other models like CoDi and NExT-GPT. The model's architecture effectively captures and predicts dynamics across different modalities, ensuring high accuracy in challenging multimodal scenarios.

Additionally, the application of WorldGPT as a multimodal instruction synthesizer was validated. Agents fine-tuned with synthetic instructions produced by WorldGPT displayed comparable performance to those refined with real-world data, underscoring the reliability of WorldGPT as a world simulator.

Implications and Future Directions

The inclusion of cognitive architectural elements poised for enhancement marks a significant leap toward creating context-aware multimodal world models. This integration paves the way for future endeavors involving the blending of LLMs with multimodal contexts, promising more nuanced interactions with complex environments.

As for practical implications, the ability of WorldGPT to synthesize instructional data broadens its applicability in domains where annotated data is scarce or costly to obtain. Future developments could focus on expanding the cognitive framework to encompass even more complexities of human-like reasoning and knowledge integration.

In summary, the proposed WorldGPT offers a versatile and effective framework for modeling intricate world dynamics across modalities, offering promising avenues for the continued evolution of multimodal AI systems.