NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models

TL;DR

NesTools is a new dataset designed to evaluate the ability of large language models to learn and execute nested tool calls, addressing a gap in existing benchmarks and revealing current models' limitations.

Contribution

The paper introduces NesTools, a high-quality, automatically generated dataset for assessing nested tool learning in LLMs, and provides comprehensive experimental analysis.

Findings

Current LLMs struggle with complex nested tool tasks.

NesTools enables systematic evaluation of nested tool learning.

Large-scale experiments reveal limitations of existing models.

Abstract

Large language models (LLMs) combined with tool learning have gained impressive results in real-world applications. During tool learning, LLMs may call multiple tools in nested orders, where the latter tool call may take the former response as its input parameters. However, current research on the nested tool learning capabilities is still under-explored, since the existing benchmarks lack relevant data instances. To address this problem, we introduce NesTools to bridge the current gap in comprehensive nested tool learning evaluations. NesTools comprises a novel automatic data generation method to construct large-scale nested tool calls with different nesting structures. With manual review and refinement, the dataset is in high quality and closely aligned with real-world scenarios. Therefore, NesTools can serve as a new benchmark to evaluate the nested tool learning abilities of LLMs. We conduct extensive experiments on 22 LLMs, and provide in-depth analyses with NesTools, which shows that current LLMs still suffer from the complex nested tool learning task.