What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks

TL;DR

This paper empirically investigates how various internal and external factors influence the stability and performance of tool learning frameworks in large language models, highlighting the importance of exploration and trial.

Contribution

It provides a comprehensive empirical analysis of factors affecting tool learning stability, offering new insights for improving LLM tool integration.

Findings

Increased trial and exploration significantly benefit LLM tool learning.

Performance varies across tasks, datasets, and algorithms.

Empirical results highlight key factors influencing robustness.

Abstract

Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in previous works that the performance of tool learning varies from tasks, datasets, training settings, and algorithms. Without understanding the impact of these factors, it can lead to inconsistent results, inefficient model deployment, and suboptimal tool utilization, ultimately hindering the practical integration and scalability of LLMs in real-world scenarios. Therefore, in this paper, we explore the impact of both internal and external factors on the performance of tool learning frameworks. Through extensive experiments on two benchmark datasets, we find several insightful conclusions for future work, including the observation that LLMs can benefit significantly from increased trial and exploration. We believe our empirical study provides a new perspective for future tool learning research.