Are Emergent Abilities of Large Language Models a Mirage? — part 3/3

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We begin by inducing an emergent classification ability in a LeNet convolutional neural network family [ 22 ] , trained on the MNIST handwritten digits dataset [ 21 ] . This family displays smoothly increasing test accuracy as the number of parameters increase (Fig. 10 B). To emulate the accuracy metric used by emergence papers [ 8 , 33 , 28 ] , we use subset accuracy : 1 if the network classifies K K out of K K (independent) test data correctly, 0 otherwise. Under this definition of accuracy, the model family displays an “emergent” ability to correctly classify sets of MNIST digits as K K increases from 1 1 to 5 5 , especially when combined with sparse sampling of model sizes (Fig. 10 C). This convolutional family’s emergent classification ability qualitatively matches published emergent abilities, e.g., at the BIG-Bench Grounded Mappings task [ 33 ] (Fig. 10 A).

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