The current literature on large language model (LLM)-based evaluation metrics for healthcare chatbots.
By Dr. Sushama R. Chaphalkar, PhD.Apr 2 2024Reviewed by Lily Ramsey, LLM In a recent article published in npj Digital Medicine, researchers explored the current literature on large language model -based evaluation metrics for healthcare chatbots.
Background Artificial intelligence , especially in healthcare chatbots, revolutionizes patient care by enabling interactive, personalized, and proactive assistance across various medical tasks and services. Existing evaluation metrics for LLMs The evaluation of language models involves intrinsic and extrinsic methods, which may be automatic or manual. Intrinsic metrics assess the proficiency in generating coherent sentences, while extrinsic metrics gauge the performance in a real-world context.
Essential metrics for evaluating healthcare chatbots In the present paper, the researchers outlined a comprehensive set of metrics for the user-centered evaluation of LLM-based healthcare chatbots, aiming to distinguish this approach from existing studies. Trustworthiness metrics encompass safety, privacy, bias, and interpretability, which are crucial for responsible AI.
Metrics association involves within-category and between-category relations, impacting metric correlations. For instance, within accuracy metrics, up-to-dateness positively correlates with groundedness.
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