RefTool: Enhancing Model Reasoning with Reference-Guided Tool Creation
- URL: http://arxiv.org/abs/2505.21413v1
- Date: Tue, 27 May 2025 16:41:19 GMT
- Title: RefTool: Enhancing Model Reasoning with Reference-Guided Tool Creation
- Authors: Xiao Liu, Da Yin, Zirui Wu, Yansong Feng,
- Abstract summary: RefTool is a reference-guided framework for automatic tool creation.<n>It generates executable tools from reference content, validate them using illustrative examples, and organize them hierarchically into a toolbox.<n> Experiments on causality, physics, and chemistry benchmarks demonstrate that RefTool outperforms existing tool-creation and domain-specific reasoning methods.
- Score: 44.128974924517465
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Tools enhance the reasoning capabilities of large language models (LLMs) in complex problem-solving tasks, but not all tasks have available tools. In the absence of predefined tools, prior works have explored instructing LLMs to generate tools on their own. However, such approaches rely heavily on the models' internal knowledge and would fail in domains beyond the LLMs' knowledge scope. To address this limitation, we propose RefTool, a reference-guided framework for automatic tool creation that leverages structured external materials such as textbooks. RefTool consists of two modules: (1) tool creation, where LLMs generate executable tools from reference content, validate them using illustrative examples, and organize them hierarchically into a toolbox; and (2) tool utilization, where LLMs navigate the toolbox structure to select and apply the appropriate tools to solve problems. Experiments on causality, physics, and chemistry benchmarks demonstrate that RefTool outperforms existing tool-creation and domain-specific reasoning methods by 11.3% on average accuracy, while being cost-efficient and broadly generalizable. Analyses reveal that grounding tool creation in references produces accurate and faithful tools, and that the hierarchical structure facilitates effective tool selection. RefTool enables LLMs to overcome knowledge limitations, demonstrating the value of grounding tool creation in external references for enhanced and generalizable reasoning.
Related papers
- Tool Unlearning for Tool-Augmented LLMs [14.755831733659699]
Tool-augmented large language models (LLMs) are often trained on datasets of query-response pairs.<n>ToolDelete is the first approach for unlearning tools from tool-augmented LLMs.
arXiv Detail & Related papers (2025-02-03T05:50:55Z) - PTR: Precision-Driven Tool Recommendation for Large Language Models [43.53494041932615]
We propose a Precision-driven Tool Recommendation (PTR) approach for Large Language Models (LLMs)
PTR captures an initial, concise set of tools by leveraging historical tool bundle usage and dynamically adjusts the tool set by performing tool matching.
We present a new dataset, RecTools, and a metric, TRACC, designed to evaluate the effectiveness of tool recommendation for LLMs.
arXiv Detail & Related papers (2024-11-14T17:33:36Z) - Enhancing Tool Retrieval with Iterative Feedback from Large Language Models [9.588592185027455]
Large language models (LLMs) can effectively handle a certain amount of tools through in-context learning or fine-tuning.
In real-world scenarios, the number of tools is typically extensive and irregularly updated, emphasizing the necessity for a dedicated tool retrieval component.
We propose to enhance tool retrieval with iterative feedback from the large language model.
arXiv Detail & Related papers (2024-06-25T11:12:01Z) - Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
arXiv Detail & Related papers (2024-05-26T11:40:58Z) - 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) - 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) - MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use [79.87054552116443]
Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities.<n>We introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools.<n>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.