who attempt to explain this capability in terms of
Bayesian inference, that by Li et al. (2023) who
propose a PAC based framework for explaining this
capability and, Jiang (2023)2, Wang et al. (2023),
and Wies et al. (2023) who provide an explanation
based on latent space theory. Pertinent to this study,
though, is the overarching observation in these the-
oretical explorations that in-context learning be-
comes more powerful with increased scale, and so
is predictable and thus not emergent or indicative
of the possibility of the emergence of other latent
hazardous abilities. As mentioned earlier, how-
ever, the specific mechanisms governing in-context
learning do not impact our argument: The fact of
its functionality suffices to underscore the neces-
sity of assessing emergent abilities in the absence
of in-context learning. This becomes particularly
relevant considering that the capacity of LLMs to
execute in-context learning with flipped labels (Wei
AreEmergentAbilitiesinLarge Language Models just In-Context