Context Example

In-context learning (ICL) focuses on enabling large language models (LLMs) to perform new tasks using only a few example demonstrations within the input prompt, without parameter updates. Current research emphasizes improving ICL's effectiveness by optimizing the selection and ordering of these examples, often employing transformer-based architectures and algorithms like Bayesian networks or submodular optimization to identify the most informative examples. This research is significant because effective ICL could drastically reduce the need for extensive fine-tuning, leading to more efficient and adaptable LLMs across diverse applications, including machine translation, question answering, and various reasoning tasks.