EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction

• URL: http://arxiv.org/abs/2401.06201v3
• Date: Wed, 27 Mar 2024 06:31:42 GMT
• Title: EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
• Authors: Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, Deqing Yang,
• Abstract summary: EasyTool is a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction. It can significantly reduce token consumption and improve the performance of tool utilization in real-world scenarios.
• Score: 56.02100384015907
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Chain of Tools: Large Language Model is an Automatic Multi-tool Learner [54.992464510992605]
  Automatic Tool Chain (ATC) is a framework that enables the large language models (LLMs) to act as a multi-tool user. To scale up the scope of the tools, we next propose a black-box probing method. For a comprehensive evaluation, we build a challenging benchmark named ToolFlow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error [54.954211216847135]
  Existing large language models (LLMs) only reach a correctness rate in the range of 30% to 60%. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE) STE orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory.
  arXiv  Detail & Related papers  (2024-03-07T18:50:51Z)
• ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph [43.95759808077083]
  Existing in-context learning approaches simply format tools into a list of plain text descriptions and input them to large language models. This paper proposes ToolNet, a plug-and-play framework that scales up the number of tools to thousands with a moderate increase in token consumption.
  arXiv  Detail & Related papers  (2024-02-29T02:04:00Z)
• Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models [26.28459880766842]
  We propose a decision-aware and generalizable tool-usage framework (DEER) Specifically, we first construct the tool-usage samples with multiple decision branches via an automatic generation pipeline. Our proposed DEER is effective and significantly outperforms baselines across various datasets.
  arXiv  Detail & Related papers  (2024-02-26T16:11:03Z)
• MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning [38.610185966889226]
  We propose MLLM-Tool, a system incorporating open-source large language models and multi-modal encoders. The learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. Experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions.
  arXiv  Detail & Related papers  (2024-01-19T14:44:37Z)
• MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use [82.24774504584066]
  Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. We introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. We conduct experiments involving eight popular LLMs and find that the majority of them still struggle to effectively select tools.
  arXiv  Detail & Related papers  (2023-10-04T19:39:26Z)
• Large Language Models as Tool Makers [85.00361145117293]
  We introduce a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving. Our approach consists of two phases: 1) tool making: an LLM acts as the tool maker that crafts tools for a set of tasks. 2) tool using: another LLM acts as the tool user, which applies the tool built by the tool maker for problem-solving.
  arXiv  Detail & Related papers  (2023-05-26T17:50:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.