Summary of A Proposed S.c.o.r.e. Evaluation Framework For Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability, by Ting Fang Tan et al.
A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
by 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
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a comprehensive qualitative evaluation framework for large language models (LLMs) in healthcare, moving beyond traditional accuracy and quantitative metrics. The S.C.O.R.E. framework consists of five key aspects: Safety, Consensus, Objectivity, Reproducibility, and Explainability. These criteria aim to ensure that LLM-based models are safe, reliable, trustworthy, and ethical for clinical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test big language models in healthcare. Instead of just looking at how well they work, the researchers suggest evaluating them based on safety, agreement among experts, objectivity, reproducibility, and how well they explain their decisions. This helps ensure that these models are reliable and trustworthy for making important medical decisions. |
