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Large Language Models (LLMs) have proven effective at In-Context Learning(ICL), an ability that allows them to create predictors from labeled examples.Few studies have explored the interplay between ICL and specific properties offunctions it attempts to approximate. In our study, we use a formal frameworkto explore ICL and propose a new task of approximating functions with varyingnumber of minima. We implement a method that allows for producing functionswith given inputs as minima. We find that increasing the number of minimadegrades ICL performance. At the same time, our evaluation shows that ICLoutperforms 2-layer Neural Network (2NN) model. Furthermore, ICL learns fasterthan 2NN in all settings. We validate the findings through a set of few-shotexperiments across various hyperparameter configurations.

In-Context Learning Functions with Varying Number of Minima