HDCEval splits medical answer grading into relevance, correctness, and expression checks, uses reward-token-trained expert models, and reports improved agreement with human doctors.
In Proceed- ings of the 2020 Conference on Empirical Methods in Natu- ral Language Processing (EMNLP), 9241–9250
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Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation
HDCEval splits medical answer grading into relevance, correctness, and expression checks, uses reward-token-trained expert models, and reports improved agreement with human doctors.