ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs

Large language models face a bottleneck in tool retrieval when deployed as agents over large catalogs of tools. Although embedding-based retrieval approaches can be efficient, they may not fully capture the specialized semantics of tools. To address this, a parameterized tool retrieval approach has been developed, encoding each tool as a virtual token that is appended to the model’s vocabulary. The model is then fine-tuned in two stages to use it as a retriever. However, traditional tool retrieval tests may not reveal whether the model truly understands the tools. ToolSense, an open-source diagnostic framework, has been introduced to automatically generate three tests to evaluate the model’s tool understanding. When applied to a large tool catalog, a disassociation between tool knowledge and retrieval was found in some models, suggesting that despite their good retrieval performance, some models may not actually comprehend the tools. This news highlights the need to evaluate tool understanding in language models, which could have implications for the development of e-commerce systems and marketplaces, such as open-garage, that require precise understanding of tools and products. Moreover, the ability to assess tool understanding is crucial for improving efficiency and accuracy in tool and product retrieval in these systems.

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