[AARR] From Few to Many Shots: Exploring the Depths of In-Context Learning

The Align AI Research Review discusses a study on Many-Shot In-Context Learning, which involves introducing large language models (LLMs) with hundreds or thousands of samples at inference to learn new tasks. This approach shows potential for enhancing model performance and overcoming biases. Reinforced ICL generates reasoning chains using LLMs compared to human responses, while unsupervised ICL inputs problems directly into the model. Both methodologies demonstrate efficacy in the many-shot regime, particularly when applied to complex reasoning tasks. The research highlights the potential of many-shot learning for overcoming biases and learning high-dimensional data with numerical inputs, but also emphasizes the need for further investigation in this domain.