The provided text describes a novel approach to in-context learning (ICL) called Mixtures of In-Context Learners (MOICL) that addresses key limitations of traditional ICL, such as context length constraints and sensitivity to noisy or out-of-distribution demonstrations. MOICL partitions a set of demonstrations into subsets, trains each subset as an "expert," and learns a weighting function to combine their predictions. The authors demonstrate that MOICL outperforms traditional ICL and other baselines in classification tasks across various datasets, achieving higher accuracy while being more robust to noisy data and label imbalance. They also show that MOICL is more data and computationally efficient, making it a promising approach for improving the effectiveness of ICL.