Summary of Menti: Bridging Medical Calculator and Llm Agent with Nested Tool Calling, by Yakun Zhu et al.
MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling
by Yakun Zhu, Shaohang Wei, Xu Wang, Kui Xue, Xiaofan Zhang, Shaoting Zhang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces MeNTi, a universal agent architecture that integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance the utilization of Large Language Models (LLMs) in medical calculators. The authors focus on downstream tasks such as calculator selection, slot filling, and unit conversion, which are crucial for quantitative assessment throughout the clinical process. To evaluate the capabilities of LLMs in this context, the paper proposes CalcQA, a benchmark that requires LLMs to use medical calculators to perform calculations and assess patient health status. The experimental results show significant performance improvements with MeNTi framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MeNTi is a new way for computers to understand complex medical tasks. This helps doctors and nurses by letting them use language models like Google’s BERT to solve problems in medicine. The team created a special tool called CalcQA, which tests how well these language models can do medical calculations and help with patient care. They tested MeNTi on 100 real-life scenarios and found that it works really well. |
Keywords
» Artificial intelligence » Bert
