Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph

ACL ARR 2025 February Submission7891 Authors

Abstract:

Teaching large language models (LLMs) to use tools is crucial for improving their problem-solving abilities and expanding their applications. However, effectively using tools is challenging because it requires a deep understanding of tool functionalities and user intentions. Previous methods relied mainly on LLMs to generate instruction data, but the quality of these data was often insufficient.
In this paper, we propose a new method that uses knowledge graphs to generate high-quality instruction data for LLMs. Knowledge graphs are manually curated datasets rich in semantic information. We begin by extracting various query pathways from a given knowledge graph, which are transformed into a broad spectrum of user queries. We then translate the relationships between entities into actionable tools and parse the pathways of each query into detailed solution steps, thereby creating high-quality instruction data.
Our experiments show that LLMs fine-tuned with this data significantly improve their tool utilization and overall capabilities.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: data-efficient training

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 7891