In-Context Learning Functions with Varying Number of Minima

Since at In-Context Learning (ICL), an ability generative LLMs make predictions based on the given that allows them to create predictors from labeled prompt (i.e., the context), there is a natural relationship examples. Few studies have explored the interplay between prompt engineering and ICL (illustrated in Figure between ICL and specific properties of functions 1). The IO prompting paper introduced InstructGPT, a it attempts to approximate. In our study, we use a model that was trained to follow instructions, which was one formal framework to explore ICL and propose a of the first works to popularize ICL. The term was originally new task of approximating functions with varying introduced in the GPT-3 paper (Brown et al., 2020).