Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists

TL;DR

This paper introduces the Hierarchical Tool Error Checklist (HiTEC) framework to diagnose and reduce tool-calling errors in large language models, significantly improving their accuracy without extensive real-world interactions.

Contribution

The paper presents a novel hierarchical error checklist approach and two deployment methods, HiTEC-ICL and HiTEC-KTO, for enhancing tool-calling accuracy in LLMs.

Findings

Significant improvement in parameter-filling accuracy.

Enhanced tool-calling success rates across multiple datasets.

Effective error diagnosis without real-world interaction.

Abstract

Large language models (LLMs) have significantly advanced natural language processing, particularly through the integration of external tools and APIs. However, their effectiveness is frequently hampered by parameter mis-filling during tool calling. In this paper, we propose the Hierarchical Tool Error Checklist (HiTEC) framework to systematically diagnose and mitigate tool-calling errors without relying on extensive real-world interactions. HiTEC introduces a two-tiered approach: a global error checklist that identifies common, cross-tool issues, and a local error checklist that targets tool-specific and contextual failures. Building on this structure, we propose two deployments: HiTEC-In Context Learning (HiTEC-ICL) and HiTEC-Kahneman-Tversky Optimization (HiTEC-KTO). HiTEC-ICL embeds the global checklist in the initial prompts and leverages a two-round conversational interaction to dynamically refine parameter handling, while HiTEC-KTO generates high-quality negative examples to drive fine-tuning via preference-based optimization. Extensive experiments across five public datasets demonstrate that our framework significantly improves parameter-filling accuracy and tool-calling success rates compared to baseline methods.