A clear trend of continuous performance gains is evident as the number of few-shot samples is increased, demonstrating consistent improvements at each stage of the process (from zero- to one-, one- to three-, and three- to five-shot prompting) for almost all labels (LYM, MUC, NORM, STR), with the exception of debris (Fig. 4C ).
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In-context learning enables multimodal large language models to classify cancer pathology images.
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