LLM With Tools: A Survey

Recent advances in large language models (LLMs) such as GPT-3, GPT-4, and specialized models like Chameleon have demonstrated remarkable capabilities in natural language understanding, reasoning, and knowledge retrieval. Prior works like WebGPT and Toolformer introduced methods for integrating external tools, such as search engines and calculators, to improve accuracy and real-time performance. However, these approaches often rely on static tool invocation or limited multi-tool coordination, restricting their ability to handle complex, multi-step tasks. The emergence of multimodal data and multi-agent simulation techniques has opened new avenues for enhancing model robustness and generalization. Despite progress, challenges persist in dynamic tool selection, autonomous tool creation, and managing large toolsets efficiently, especially in real-world scenarios demanding high accuracy and adaptability.

The core challenge lies in enabling large models to effectively decide when, which, and how to invoke external tools during complex reasoning processes. Existing methods lack a unified framework for dynamic tool management, leading to inefficiencies and errors. Additionally, models struggle with generalizing tool usage across diverse tasks and domains, limiting their practical deployment. The difficulty in balancing real-time performance, accuracy, and scalability further complicates this landscape. Developing models that can autonomously create new tools and adapt to unseen scenarios remains an open problem, crucial for advancing toward artificial general intelligence (AGI). Addressing these issues requires innovative algorithms, robust training strategies, and scalable architectures.

This work introduces a function-based paradigm for tool integration, encompassing functions like fintent, fplan, and fexec to systematically manage tool invocation. It innovates by combining multimodal data and multi-agent simulation to generate diverse training datasets, enhancing generalization. The framework supports dynamic plan adjustment and error correction, improving robustness. Additionally, the paper explores enabling models to autonomously generate new tools, shifting from passive usage to active creation. These innovations collectively address the limitations of prior static or single-tool methods, offering a scalable, flexible approach adaptable to various complex tasks and domains.

• �� Define fintent to interpret user instructions and identify intent;
• �� Use fplan to generate a tool invocation plan based on intent and available tools;
• �� Execute plan via fexec, updating environment state;
• �� Collect feedback with ffeedback, process with fperceive;
• �� Dynamically adjust plan using fadjust based on feedback;
• �� Incorporate multimodal data (images, text) to enrich training samples;
• �� Employ multi-agent simulation to generate diverse, realistic tool usage scenarios;
• �� Fine-tune models on multi-task, multimodal datasets to improve understanding and control;
• �� Validate via experiments on ScienceQA, comparing accuracy, robustness, and generalization.

The primary dataset is ScienceQA, with baseline models lacking external tool integration. The enhanced models incorporate multi-tool and multimodal data, trained via multi-task fine-tuning. Metrics include accuracy, error rate, and generalization performance. Ablation studies analyze the impact of multi-tool collaboration, data diversity, and plan adjustment mechanisms. Hyperparameters such as learning rate, number of fine-tuning epochs, and tool invocation thresholds are optimized. Additional tests evaluate robustness across different tasks and unseen scenarios, confirming the framework’s effectiveness in real-world applications.

Accuracy on ScienceQA improved from 78.2% to 85.4%, with multi-tool models outperforming single-tool counterparts by 7.2% on average. Multi-modal data augmentation enhanced task generalization, especially in unseen problem types. Multi-agent simulation generated diverse training samples, reducing error rates by 15%. Fine-tuning with multi-task, multimodal datasets increased adaptability to new tasks by 20%. These results demonstrate the framework’s effectiveness in complex reasoning and real-world scenarios.

该方法适用于需要高精度和实时反应的专业场景，如医学诊断、法律分析和科研辅助。模型通过调用工具实现复杂数据处理、知识检索和推理，显著提升行业效率。未来，结合自主工具创造能力，将推动智能系统自主解决新问题，拓展应用范围。

当前框架在工具集极大时可能出现性能瓶颈，模型在超大规模工具集下表现待优化。多模态数据成本较高，实际部署受限。复杂任务中的推理错误仍存在，极端场景下鲁棒性不足。未来需提升工具管理效率，降低成本，增强自主创新能力。