A Proposed SCORE Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
<|reference_start|>A Proposed SCORE Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability: 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.<|reference_end|>
@article{tan2024a,
    title={A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models :
  Safety, Consensus, Objectivity, Reproducibility and Explainability},
    author={Ting Fang Tan, Kabilan Elangovan, Jasmine Ong, Nigam Shah, Joseph
  Sung, Tien Yin Wong, Lan Xue, Nan Liu, Haibo Wang, Chang Fu Kuo, Simon
  Chesterman, Zee Kin Yeong, Daniel SW Ting},
    journal={arXiv preprint arXiv:2407.07666},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07666},
    primaryClass={cs.CL cs.AI}
}