Google has developed a new evaluation framework to help health systems assess large language models more efficiently and reliably.  The framework, called Adaptive Precise Boolean rubrics, converts complex evaluation tasks into yes-or-no questions tailored to each query. It aims to reduce dependence on expert reviews while improving scoring consistency, according to an Aug. 26 news release from the company. The approach was tested in metabolic health use cases, including diabetes, cardiovascular disease and obesity.

Compared to traditional Likert scales, the tool improved inter-rater reliability and cut evaluation time by more than 50%. It also showed stronger sensitivity to subtle changes in response quality, including scenarios in which patient data was deliberately omitted from LLM prom

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