Tool-Planner: Task Planning with Clusters across Multiple Tools

• URL: http://arxiv.org/abs/2406.03807v2
• Date: Wed, 02 Oct 2024 16:00:39 GMT
• Title: Tool-Planner: Task Planning with Clusters across Multiple Tools
• Authors: Yanming Liu, Xinyue Peng, Jiannan Cao, Shi Bo, Yuwei Zhang, Xuhong Zhang, Sheng Cheng, Xun Wang, Jianwei Yin, Tianyu Du,
• Abstract summary: We propose Tool-Planner, a task-processing framework based on toolkits. Tool-Planner groups tools based on the API functions with the same function into a toolkit and allows LLMs to implement planning across the various toolkits.
• Score: 29.278169900986434
• License:
• Abstract: Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the paradigm of tool learning. Tool learning involves providing examples of tool usage and their corresponding functions, allowing LLMs to formulate plans and demonstrate the process of invoking and executing each tool. LLMs can address tasks that they cannot complete independently, thereby enhancing their potential across different tasks. However, this approach faces two key challenges. First, redundant error correction leads to unstable planning and long execution time. Additionally, designing a correct plan among multiple tools is also a challenge in tool learning. To address these issues, we propose Tool-Planner, a task-processing framework based on toolkits. Tool-Planner groups tools based on the API functions with the same function into a toolkit and allows LLMs to implement planning across the various toolkits. When a tool error occurs, the language model can reselect and adjust tools based on the toolkit. Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method. Our code is public at \url{https://github.com/OceannTwT/Tool-Planner}

Related papers

• StepTool: A Step-grained Reinforcement Learning Framework for Tool Learning in LLMs [44.906714156993694]
  We introduce StepTool, a novel step-grained reinforcement learning framework to improve tool learning in Large Language Models. StepTool significantly outperforms existing methods in multi-step, tool-based tasks.
  arXiv  Detail & Related papers  (2024-10-10T09:23:26Z)
• 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)
• 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)
• EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction [56.02100384015907]
  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.
  arXiv  Detail & Related papers  (2024-01-11T15:45:11Z)
• ControlLLM: Augment Language Models with Tools by Searching on Graphs [97.62758830255002]
  We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving real-world tasks. Our framework comprises three key components: (1) a textittask decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a textitThoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph; and (3) an textitexecution engine with a rich toolbox that interprets the solution path and runs the
  arXiv  Detail & Related papers  (2023-10-26T21:57:21Z)
• 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)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• 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)
• ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings [25.5476046472217]
  Augmenting large language models with external tools has emerged as a promising approach to solving complex problems. Recent in-context learning paradigm alleviates these issues, but the limited context length only allows for a few shots of demonstrations. We propose an alternative approach, $textbfToolkenGPT$, which combines the benefits of both sides.
  arXiv  Detail & Related papers  (2023-05-19T09:54:21Z)

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.