LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents (2311.05437v1)

Abstract: LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models. It maintains a skill repository of pre-trained vision and vision-LLMs and can activate relevant tools based on users' inputs to fulfill real-world tasks. LLaVA-Plus is trained on multimodal instruction-following data to acquire the ability to use tools, covering visual understanding, generation, external knowledge retrieval, and compositions. Empirical results show that LLaVA-Plus outperforms LLaVA in existing capabilities and exhibits new ones. It is distinct in that the image query is directly grounded and actively engaged throughout the entire human-AI interaction sessions, significantly improving tool use performance and enabling new scenarios.

• The paper presents a modular architecture that leverages dynamic tool activation to boost multimodal reasoning and visual understanding.
• The paper employs an innovative training regime with a newly curated dataset to enable autonomous tool selection during complex human-AI interactions.
• The paper demonstrates strong numerical improvements on benchmarks like LLaVA-Bench and SEED-Bench, enhancing performance in visual conversation, reasoning, and recognition.

Overview of LLaVA-Plus: Enhancing Multimodal Agents with Tool-Driven Capabilities

The paper introduces LLaVA-Plus, a versatile multimodal framework that extends the capabilities of Large Multimodal Models (LMMs) via an innovative approach combining end-to-end training and tool-chaining methodologies. The primary ambition is to create a robust multimodal assistant capable of executing a wide range of complex vision and vision-language tasks by dynamically leveraging pre-trained vision models as tools.

Core Contributions

The LLaVA-Plus architecture showcases a modular system design where a foundational LMM anticipates user requests and leverages a skill repository of vision and LLMs. This is a significant step beyond current multimodal frameworks that predominantly focus on either end-to-end training or enforcing tool usage through prompt engineering. By training LLaVA-Plus on a newly curated dataset for multimodal instruction following, the model can dynamically select and activate the appropriate tools on-the-fly, thus addressing diverse real-world challenges.

Methodological Advances

1. Model Architecture and Training: The training process integrates both traditional image-text pairs and a sophisticated format allowing for tool activation when necessary. This involves a contiguous session of human-assistant interaction where the assistant autonomously determines when to invoke a specific tool from its skill repository.
2. Skill Repository: The framework incorporates an extensive skill repository covering:
   • Vision-specific tasks like detection, segmentation, tagging, captioning, and Optical Character Recognition (OCR).
   • Knowledge retrieval harnessed from external sources.
   • Image generation and editing through models like Stable Diffusion.
   • Visual prompts facilitating interactive image editing and multimodal tasks.
3. Data Generation: A new dataset aligns with the model architecture, capturing tool use in human-AI sessions. It encompasses various visual understanding and cognitive tasks enriched with tool-driven scenarios, consistently enhancing the model's competency in real-time decision-making contexts.

Evaluation and Strong Numerical Results

Experiments demonstrate LLaVA-Plus made notable advancements compared to its predecessors, such as LLaVA. LLaVA-Plus exhibits substantial performance enhancements across various benchmarks, notably:

• On LLaVA-Bench, the model improved visual conversation, detailed description, and reasoning benchmarks, clearly outperforming its predecessor LLaVA.
• The introduction of tool-driven tasks presents an improvement in recognition capabilities as demonstrated on the COCO dataset and other task-specific benchmarks.
• On SEED-Bench, LLaVA-Plus consistently outperformed baseline models in perceptual and reasoning capabilities.

These results underline the efficacy of the tool integrations, reinforcing their utility in enhancing multimodal task performances.

Implications and Future Directions

The implications of LLaVA-Plus extend to the broader AI development landscape, signaling a shift toward more modular and capable AI systems. By dynamically selecting and utilizing various specialized tools, this framework emphasizes a path forward in creating adaptable, intelligent systems capable of handling complex, real-world challenges.

The potential future extensions include:

• Expanding the skill repository to include more specific domain-oriented tools, which could enhance applicability across various verticals.
• Further enhancements in model training to reduce hallucinations and improve robustness in tool selection, addressing practical concerns around reliability and accuracy.

LLaVA-Plus stands as a testament to the evolution of AI agents into more comprehensive assistants, significantly advancing the field of multimodal AI by illustrating a practical approach to integrate and utilize diverse cognitive tools effectively.