June 7, 2024, 4:51 a.m. | Eden Avnat, Michal Levy, Daniel Herstain, Elia Yanko, Daniel Ben Joya, Michal Tzuchman Katz, Dafna Eshel, Sahar Laros, Yael Dagan, Shahar Barami, Jose

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.03855v1 Announce Type: new
Abstract: Clinical problem-solving requires processing of semantic medical knowledge such as illness scripts and numerical medical knowledge of diagnostic tests for evidence-based decision-making. As large language models (LLMs) show promising results in many aspects of language-based clinical practice, their ability to generate non-language evidence-based answers to clinical questions is inherently limited by tokenization. Therefore, we evaluated LLMs' performance on two question types: numeric (correlating findings) and semantic (differentiating entities) while examining differences within and between LLMs …

abstract arxiv benchmarking clinical cs.cl decision diagnostic evidence knowledge language language models large language large language models llms making medical numerical performance practice problem problem-solving processing results scripts semantic show tests type

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