ToolExpNet: Optimizing Multi-Tool Selection in LLMs with Similarity and Dependency-Aware Experience Networks

ACL ARR 2025 February Submission1856 Authors

Abstract: Tool learning enhances Large Language Models' (LLMs) dynamic interaction with external tools, improving their ability to solve complex problems. However, current empirical methods, which primarily focus on isolated tools learning, still struggle with accurate multi-tool selection due to issues like confusing similar tools and neglecting dependencies. To address these challenges, we propose the Tool Experience Network (ToolExpNet), which integrates tools and trial-and-error experiences into a network characterized by semantic similarity and dependency relationships. ToolExpNet iteratively conducts simulated experiments using adaptive sampling to explore subtle differences and connections between tools, and summarizes these experiences to provide insightful guidance for LLM tool selection. Our experiments demonstrate that learning the relationships between tools helps achieve more comprehensive tool learning. Evaluations on multiple real-world API datasets show that ToolExpNet effectively addresses common challenges in multi-tool selection, significantly outperforming existing baselines across different foundation LLMs.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: task-oriented, applications, embodied agents

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 1856