EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction

ACL ARR 2024 August Submission19 Authors

Abstract:

There has been a rising interest in utilizing tools in applications of autonomous agents based on large language models (LLMs) to address intricate real-world tasks.
To develop LLM-based agents, it usually requires LLMs to understand many tool functions from different tool documentations. However, these documentations could be diverse, redundant, or incomplete, which immensely affects the capability of LLMs in using tools.
Current LLMs exhibit satisfactory instruction-following capabilities based on instruction-following fine-tuning process.
Motivated by this, in this paper, we introduce EasyTool, a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction to fully leverage instruction-following capabilities of LLMs 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 LLM-based agents on tool utilization in real-world scenarios.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: corpus creation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study, Data resources, Data analysis

Languages Studied: English

Submission Number: 19