Imagine learning complex concepts with just a single example. That’s the power of in-context learning (ICL) where large language models (LLMs) like GPT-3 learn directly from a few demonstrations within the given context. However, this ability has a significant limitation: current ICL methods often require dozens of examples, which can be impractical and inefficient. New research introduces "SeCoKD," a novel self-knowledge distillation training framework designed to empower LLMs to learn efficiently from even fewer examples. SeCoKD works by aligning a "student" model with a heavily prompted "teacher" model, effectively squeezing the most learning out of each demonstration. This approach enhances the model's ability to extract knowledge from single examples. The results are impressive, showing significant improvements, particularly in zero-shot and one-shot learning scenarios. Researchers tested SeCoKD across three leading LLMs and six reasoning benchmarks, observing performance gains of up to 30% in zero-shot and 10% in one-shot settings compared to standard models. SeCoKD not only boosts performance but also increases robustness. Unlike traditional fine-tuning methods that often struggle with new tasks, SeCoKD-trained models maintain their performance across diverse challenges. Furthermore, SeCoKD simplifies complex tasks by effectively converting difficult queries into easier ones when provided with the same demonstration. This is a significant advancement in ICL, highlighting the potential of knowledge distillation for efficient and robust learning in LLMs. The research opens doors to new applications where data is scarce or expensive to obtain, paving the way for more efficient and robust AI systems.