Recently, the integration of external tools with Large Language Models (LLMs)
has emerged as a promising approach to overcome the inherent constraints of
their pre-training data. However, realworld applications often involve a
diverse range of tools, making it infeasible to incorporate all tools directly
into LLMs due to constraints on input length and response time. Therefore, to
fully exploit the potential of tool-augmented LLMs, it is crucial to develop an
effective tool retrieval system. Existing tool retrieval methods techniques
mainly rely on semantic matching between user queries and tool descriptions,
which often results in the selection of redundant tools. As a result, these
methods fail to provide a complete set of diverse tools necessary for
addressing the multifaceted problems encountered by LLMs. In this paper, we
propose a novel modelagnostic COllaborative Learning-based Tool Retrieval
approach, COLT, which captures not only the semantic similarities between user
queries and tool descriptions but also takes into account the collaborative
information of tools. Specifically, we first fine-tune the PLM-based retrieval
models to capture the semantic relationships between queries and tools in the
semantic learning stage. Subsequently, we construct three bipartite graphs
among queries, scenes, and tools and introduce a dual-view graph collaborative
learning framework to capture the intricate collaborative relationships among
tools during the collaborative learning stage. Extensive experiments on both
the open benchmark and the newly introduced ToolLens dataset show that COLT
achieves superior performance. Notably, the performance of BERT-mini (11M) with
our proposed model framework outperforms BERT-large (340M), which has 30 times
more parameters. Additionally, we plan to publicly release the ToolLens dataset
to support further research in tool retrieval.

通过 COLT 模型，可以实现与用户查询语义相似并具有协同关系的工具的检索，该模型在工具检索方面的性能超过了参数更多的 BERT-large (340M) 模型，并公开发布了 ToolLens 数据集以支持进一步的工具检索研究。