<p> GPT-3-ENS and human labeled data improves metrics over models trained only using human data. We show that using a very small number of human labeled examples, 210, we are able to produce more medically correct and better quality summaries than using roughly thirty times as many human labeled examples for two different summarization models. GPT-3-ENS data. Figure 3 and Figure 4 show that doctors prefer summaries from the model trained on the mixture data over those produced by models trained on human or GPT-3-ENS data alone, in terms of amount of medical information captured as well as the overall quality of the summary. At the heart of the approach is a medically aware ensembling criterion that ensembles multiple summaries for an input from a powerful low-shot learner such as GPT-3.</p>

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Last-modified: 2022-02-18 (金) 13:21:35 (128d)