The associated paper and its abstract:

Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, one can choose a metric which leads to the inference of an emergent ability or another metric which does not. Thus, our alternative suggests that existing claims of emergent abilities are creations of the researcher’s analyses, not fundamental changes in model behavior on specific tasks with scale. We present our explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities, (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show how similar metric decisions suggest apparent emergent abilities on vision tasks in diverse deep network architectures (convolutional, autoencoder, transformers). In all three analyses, we find strong supporting evidence that emergent abilities may not be a fundamental property of scaling AI models.

Page two of the paper states their thesis pretty succinctly:

In this paper, we call into question the claim that LLMs possess emergent abilities, by which we specifically mean sharp and unpredictable changes in model outputs as a function of model scale on specific tasks. Our doubt is based on the observation that emergent abilities seem to appear only under metrics that nonlinearly or discontinuously scale any model’s per-token error rate.

And figure two walks through the argument for it concisely as well:

(OCR for image: Figure 2: Emergent abilities of large language models are creations of the researcher’s analyses, not fundamental changes in model outputs with scale. (A) Suppose the per-token cross-entropy loss decreases monotonically with model scale, e.g., LCE scales as a power law. (B) The per-token probability of selecting the correct token asymptotes towards 1 with increasing model scale. © If the researcher scores models’ outputs using a nonlinear metric such as Accuracy (which requires a sequence of tokens to all be correct), the researcher’s measurement choice nonlinearly scales performance, causing performance to change sharply and unpredictably in a manner that qualitatively matches published emergent abilities (inset). (D) If the researcher instead scores models’ outputs using a discontinuous metric such as (Multiple Choice Grade, which is similar to a step function), the researcher’s measurement choice discontinuously scales performance, causing performance to change sharply and unpredictably in a manner that qualitatively matches published emergent abilities (inset). (E) Changing from a nonlinear metric to a linear metric (such as Token Edit Distance), model shows smooth, continuous and predictable improvements, ablating the emergent ability. (F) Changing from a discontinuous metric to a continuous metric (e.g. Brier Score) again reveals smooth, continuous and predictable improvements in task performance, ablating the emergent ability. Consequently, emergent abilities may be creations of the researcher’s analyses, not fundamental changes in model family behavior on specific tasks.)

But alas, AI/ML isn’t my wheelhouse, so the paper quickly starts to go over my head.