of-thought prompting. In-context learning is the
technique in which LLMs are provided with a lim-
ited number of examples from which they learn
how to perform a task (Brown et al., 2020; Liu et al.,
2023). Recent investigations into the theoretical un-
derpinnings of in-context learning and its specific
manifestation in LLMs indicate that it might bear
resemblance to the process of fine-tuning (Akyürek
et al., 2022; Dai et al., 2023; Von Oswald et al.,
2023; Wei et al., 2023). Another technique exclu-
sive to LLMs is instruction tuning, alternatively
known as instructional fine-tuning. This technique
involves training LLMs on datasets of instructional
content, which enables the models to follow ex-
plicit instructions in prompts (Chung et al., 2022;
Wei et al., 2022a; Taori et al., 2023) (see also Sec-
tion 2). Finally, chain-of-thought prompting is the
technique in which models are provided with a se-
quence of intermediate reasoning steps to boost
AreEmergentAbilitiesinLarge Language Models just In-Context