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MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning (2401.10727v2)

Published 19 Jan 2024 in cs.CV

Abstract: Recently, the astonishing performance of LLMs in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. However, the current LLMs' perceiving tool-use ability is limited to a single text query, which may result in ambiguity in understanding the users' real intentions. LLMs are expected to eliminate that by perceiving the visual- or auditory-grounded instructions' information. Therefore, in this paper, we propose MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. To facilitate the evaluation of the model's capability, we collect a dataset featured by consisting of multi-modal input tools from HuggingFace. Another important feature of our dataset is that our dataset also contains multiple potential choices for the same instruction due to the existence of identical functions and synonymous functions, which provides more potential solutions for the same query. The experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/MLLM-Tool/MLLM-Tool.

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Overview of "MLLM-Tool: A Multimodal LLM For Tool Agent Learning"

The paper "MLLM-Tool: A Multimodal LLM For Tool Agent Learning" addresses the limitation of current LLMs in comprehending and utilizing external tools based solely on text inputs. The authors propose MLLM-Tool, an innovative system that integrates multimodal encoders with open-source LLMs to process and understand instructions formed from diverse modalities, including visual and auditory inputs. This advancement aims to enhance the capability of LLMs in selecting appropriate tools when faced with tasks requiring more than textual input, thus reducing ambiguity and improving accuracy in understanding user intentions.

Key Contributions

  1. Integration of Multimodal Encoders and LLMs: The system allows LLMs to be conscious of and integrate inputs across various modalities, ensuring a broader understanding of the task at hand and more precise tool selection.
  2. ToolMMBench Dataset: The authors introduce a novel dataset, ToolMMBench, compiled from HuggingFace, including more than 932 high-quality machine learning APIs. The dataset not only encompasses multifaceted modalities but also includes numerous instances where single instructions are associated with multiple potential APIs, reflecting more realistic scenarios.
  3. Performance Metrics and Evaluation:
    • The authors establish evaluation metrics that consider the specifics of multimodal inputs, ambiguity types, and varied modality combinations to comprehensively assess the model's performance.
    • Extensive experiments reveal that MLLM-Tool achieves a tool selection accuracy of 88.19%, demonstrating its effectiveness in selecting the correct tools for multimodal instructions.
  4. Fine-tuning with Low-Rank Adaptation (LoRA): The paper employs LoRA to fine-tune LLMs efficiently, optimizing performance while minimizing overhead in parameters.

Findings and Implications

  • Accuracy and Ambiguity Resolution: MLLM-Tool shows superior accuracy in resolving ambiguities brought by multimodal instructions compared to traditional text-only instruction following, underlining the importance of incorporating visual and auditory information in task execution.
  • The Model Configuration: The exploration of multiple LLM configurations, including Vicuna, Llama, and Llama2, indicates that larger models (13B) generally outperform their smaller counterparts (7B) after adequate training, highlighting the scaling advantages in multimodal contexts.
  • Practical Implications: This development could provide substantial improvements in LLM-based systems, such as virtual assistants and autonomous agents, which require interaction with diverse data forms and external systems.

Future Directions

  • Extension to More Complex Scenarios: While MLLM-Tool deals with a defined set of APIs, its methodology could extend to explore open-domain tool learning, especially as LLMs continue to evolve with better interpretative layers for varied modalities.
  • Integration with Enhanced Interaction Techniques: Implementing Chain-of-Thought prompting and ensuring compatibility with multistep and interactive task processing could offer further sophistication to the system.
  • Increased Dataset Diversity: As Transformer-based models and APIs proliferate, additional integrations of APIs within other specialized fields could enhance the robustness of such systems across broader applications.

In conclusion, MLLM-Tool exemplifies a significant stride towards equipping LLM-based systems with comprehensive multimodal capabilities, bridging the gap between human-like understanding and computational efficiency in executing tasks across diverse platforms.

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Authors (11)
  1. Chenyu Wang (65 papers)
  2. Weixin Luo (20 papers)
  3. Qianyu Chen (18 papers)
  4. Haonan Mai (1 paper)
  5. Jindi Guo (1 paper)
  6. Sixun Dong (13 papers)
  7. Xiaohua (2 papers)
  8. Xuan (3 papers)
  9. Zhengxin Li (16 papers)
  10. Lin Ma (206 papers)
  11. Shenghua Gao (84 papers)
Citations (10)
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  1. GitHub - Tool-LMM/Tool-LMM: Tool-LMM: A Large Multi-Modal Model for Tool Agent Learning (79 stars)
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