What Are Tools Anyway? A Survey from the Language Model Perspective
- URL: http://arxiv.org/abs/2403.15452v1
- Date: Mon, 18 Mar 2024 17:20:07 GMT
- Title: What Are Tools Anyway? A Survey from the Language Model Perspective
- Authors: Zhiruo Wang, Zhoujun Cheng, Hao Zhu, Daniel Fried, Graham Neubig,
- Abstract summary: Language models (LMs) are powerful yet mostly for text generation tasks.
We provide a unified definition of tools as external programs used by LMs.
We empirically study the efficiency of various tooling methods.
- Score: 67.18843218893416
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Language models (LMs) are powerful yet mostly for text generation tasks. Tools have substantially enhanced their performance for tasks that require complex skills. However, many works adopt the term "tool" in different ways, raising the question: What is a tool anyway? Subsequently, where and how do tools help LMs? In this survey, we provide a unified definition of tools as external programs used by LMs, and perform a systematic review of LM tooling scenarios and approaches. Grounded on this review, we empirically study the efficiency of various tooling methods by measuring their required compute and performance gains on various benchmarks, and highlight some challenges and potential future research in the field.
Related papers
- 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 with Large Language Models: A Survey [60.733557487886635]
Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.
Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
arXiv Detail & Related papers (2024-05-28T08:01:26Z) - 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) - Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios [93.68764280953624]
UltraTool is a novel benchmark designed to improve and evaluate Large Language Models' ability in tool utilization.
It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving.
A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage.
arXiv Detail & Related papers (2024-01-30T16:52:56Z) - 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 [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) - 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.