EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
EASYTOOL leverages content extraction and standardization to generate concise tool instructions, greatly improving LLM tool utilization efficiency.
Key Findings
Methodology
The approach collects diverse tool documents and employs pretrained language models (e.g., ChatGPT) for content filtering and restructuring. The process involves: 1) cleaning documents to remove redundancy; 2) extracting core functionalities and parameters; 3) generating standardized instructions with examples. Experiments on datasets like ToolBench and RestBench demonstrate reduced token usage and increased invocation accuracy. The framework supports multiple tools and models, significantly enhancing tool calling performance in real-world scenarios.
Key Results
- EASYTOOL reduces token count by 70.43% in ToolBench, boosting success rate from 62.3% to 69.8%. In retrieval tasks, instruction quality improved accuracy to 85.6%. The method outperforms baseline models like Vicuna and Mistral-Instruct-7B, increasing success rates by over 20%. Combining high-quality instructions enhances zero-shot and few-shot learning, reducing invocation errors.
- High-quality instructions improve models' understanding and adaptability across tasks. Experiments show success rates surpassing 70% in complex multi-tool scenarios, with notable improvements in question answering, task planning, and mathematical reasoning. The approach also enhances open-source models' tool usage, demonstrating broad applicability.
- By removing redundant info and standardizing instructions, the method boosts models' efficiency and robustness. Cross-scenario tests confirm its effectiveness in diverse applications, establishing a new paradigm for automated tool management in large language models.
Significance
This work addresses the challenge of inconsistent and verbose tool documentation hindering LLM understanding. By automating the generation of concise, standardized instructions, it significantly improves tool invocation accuracy and efficiency. This advancement mitigates the limitations of current manual or semi-automated methods, enabling scalable management of diverse tools. The approach enhances the deployment of autonomous agents in real-world applications like intelligent assistants, automated reasoning, and multi-modal systems, fostering more capable and reliable AI systems. It also opens avenues for further research into multi-source content integration and cross-lingual standardization, pushing the frontier of AI tool utilization.
Technical Contribution
The paper introduces a novel pipeline combining content purification, parameter extraction, and instruction generation using pretrained language models. It automates the transformation of heterogeneous, lengthy tool documentation into compact, high-quality instructions. The framework supports multi-tool, multi-model scenarios, demonstrating scalability and robustness. Compared to manual curation or simple compression, this method offers a systematic, end-to-end solution with theoretical guarantees of content fidelity and practical improvements in invocation success. It also integrates evaluation metrics for instruction accuracy and scenario plausibility, ensuring high-quality outputs.
Novelty
This research pioneers the systematic conversion of multi-source, verbose tool documents into concise, standardized instructions tailored for LLMs. Unlike prior work relying on manual curation or limited template-based approaches, EASYTOOL employs pretrained models for automated content filtering, core functionality extraction, and instruction synthesis. Its closed-loop design ensures high fidelity and applicability across diverse tools and domains, representing a significant step forward in automated documentation optimization for AI tool integration.
Limitations
- The method depends on the understanding capacity of pretrained models, which may struggle with highly complex or ambiguous documents, leading to incomplete or inaccurate extraction.
- Its effectiveness diminishes with highly specialized or niche tools lacking sufficient training data, requiring further domain adaptation.
- In scenarios with extremely heterogeneous or poorly structured documents, the pipeline may need manual intervention, limiting full automation. Future work should focus on multi-modal data fusion and domain-specific fine-tuning.
Future Work
Future directions include integrating multi-modal data such as images and videos to enrich tool descriptions, applying reinforcement learning to optimize instruction quality, and developing multilingual capabilities for cross-lingual standardization. Additionally, expanding the framework to handle dynamic, evolving toolsets and incorporating user feedback for continuous improvement are promising avenues. These efforts aim to realize fully autonomous, scalable, and context-aware tool management systems, further empowering large language models in complex real-world tasks.
AI Executive Summary
The rapid advancement of large language models (LLMs) has unlocked new potentials in autonomous agents capable of complex reasoning and task execution. However, their effectiveness heavily relies on the ability to utilize external tools, which are often documented in diverse, lengthy, and inconsistent formats. Traditional reliance on raw documentation hampers models’ understanding, leading to frequent errors, inefficiencies, and limited scalability.
This study introduces EASYTOOL, a novel framework designed to automatically transform heterogeneous tool documentation into concise, standardized instructions. The core idea is to leverage pretrained language models like ChatGPT to filter irrelevant content, extract essential functionalities and parameters, and generate easy-to-follow instructions with examples. This process significantly reduces token consumption—by over 70% in benchmarks—and enhances the accuracy of tool invocation.
Extensive experiments across datasets such as ToolBench and RestBench demonstrate that models utilizing EASYTOOL-generated instructions outperform baseline methods, achieving higher success rates and better retrieval accuracy. For instance, in complex multi-tool scenarios, success rates improved from around 62% to nearly 70%, with some models surpassing GPT-4 in certain tasks.
The significance of this work lies in its ability to address the long-standing challenge of inconsistent and verbose tool documentation. By automating the creation of high-quality, standardized instructions, it paves the way for more reliable, scalable, and efficient AI systems. This approach not only benefits current applications like question answering and task planning but also sets a foundation for future multi-modal, multilingual, and dynamic tool management systems.
Despite these advances, limitations remain, including dependency on pretrained model understanding and challenges with highly specialized tools. Future work aims to incorporate multi-modal data, reinforcement learning, and cross-lingual capabilities to further enhance the robustness and universality of the framework. Overall, EASYTOOL marks a significant step toward autonomous, intelligent agents capable of seamlessly integrating and utilizing diverse external tools in real-world environments.
Deep Dive
Abstract
To address intricate real-world tasks, there has been a rising interest in tool utilization in applications of large language models (LLMs). To develop LLM-based agents, it usually requires LLMs to understand many tool functions from different tool documentation. But these documentations could be diverse, redundant or incomplete, which immensely affects the capability of LLMs in using tools. To solve this, we introduce EASYTOOL, a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction for easier tool usage. EasyTool purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i.e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents. Extensive experiments on multiple different tasks demonstrate that EasyTool can significantly reduce token consumption and improve the performance of tool utilization in real-world scenarios. Our code will be available at \url{https://github.com/microsoft/JARVIS/} in the future.