  Recently, integrating external tools with Large Language Models (LLMs) has
gained significant attention as an effective strategy to mitigate the
limitations inherent in their pre-training data. However, real-world systems
often incorporate a wide array of tools, making it impractical to input all
tools into LLMs due to length limitations and latency constraints. Therefore,
to fully exploit the potential of tool-augmented LLMs, it is crucial to develop
an effective tool retrieval system. Existing tool retrieval methods primarily
focus on semantic matching between user queries and tool descriptions,
frequently leading to the retrieval of redundant, similar tools. Consequently,
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. Furthermore, we will release ToolLens publicly to facilitate
future research on tool retrieval.
