A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
Tan, Ting Fang, Elangovan, Kabilan, Ong, Jasmine, Shah, Nigam, Sung, Joseph, Wong, Tien Yin, Xue, Lan, Liu, Nan, Wang, Haibo, Kuo, Chang Fu, Chesterman, Simon, Yeong, Zee Kin, Ting, Daniel SW
–arXiv.org Artificial Intelligence
A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed. We propose 5 key aspects for evaluation of LLMs: Safety, Consensus, Objectivity, Reproducibility and Explainability (S.C.O.R.E.). We suggest that S.C.O.R.E. may form the basis for an evaluation framework for future LLM-based models that are safe, reliable, trustworthy, and ethical for healthcare and clinical applications.
arXiv.org Artificial Intelligence
Jul-10-2024
- Country:
- Asia (0.74)
- North America > United States
- California > San Francisco County > San Francisco (0.28)
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- Research Report (0.50)
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