MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling

• URL: http://arxiv.org/abs/2410.13610v1
• Date: Thu, 17 Oct 2024 14:46:22 GMT
• Title: MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling
• Authors: Yakun Zhu, Shaohang Wei, Xu Wang, Kui Xue, Xiaofan Zhang, Shaoting Zhang,
• Abstract summary: We introduce MeNTi, a universal agent architecture for Large Language Models (LLMs) MeNTi integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization. To assess the capabilities of LLMs for quantitative assessment throughout the clinical process of calculator scenarios, we introduce CalcQA.
• Score: 12.236137157144965
• License:
• Abstract: Integrating tools into Large Language Models (LLMs) has facilitated the widespread application. Despite this, in specialized downstream task contexts, reliance solely on tools is insufficient to fully address the complexities of the real world. This particularly restricts the effective deployment of LLMs in fields such as medicine. In this paper, we focus on the downstream tasks of medical calculators, which use standardized tests to assess an individual's health status. We introduce MeNTi, a universal agent architecture for LLMs. MeNTi integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization. Specifically, it achieves flexible tool selection and nested tool calling to address practical issues faced in intricate medical scenarios, including calculator selection, slot filling, and unit conversion. To assess the capabilities of LLMs for quantitative assessment throughout the clinical process of calculator scenarios, we introduce CalcQA. This benchmark requires LLMs to use medical calculators to perform calculations and assess patient health status. CalcQA is constructed by professional physicians and includes 100 case-calculator pairs, complemented by a toolkit of 281 medical tools. The experimental results demonstrate significant performance improvements with our framework. This research paves new directions for applying LLMs in demanding scenarios of medicine.

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