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Assessing the Proficiency of LLMs with Various Tasks and Evaluators
Tong Min Kim1, Youngrong Lee1, Chansik Kim1,2
1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Republic of Korea.
None:
Previous studies have been limited to giving one or two tasks to Large Language Models (LLMs) and involved a small number of evaluators within a single domain to evaluate the LLM's answer. We assessed the proficiency of four LLMs by applying eight tasks and evaluating 32 results with 17 evaluators from diverse domains, demonstrating the significance of various tasks and evaluators on LLMs.
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