In-Context Learning Explained: Why LLMs Need 100 Examples, Not 5

Author(s): MKWriteshere

Originally published on Towards AI.

New research reveals the truth about few-shot learning and what it means for your AI applications

What happens when you feed ChatGPT examples in your prompts isn’t what you think

The article explores the concept of In-Context Learning (ICL) in AI and its implications on the performance of models like ChatGPT. It discusses findings from a comprehensive study conducted by Microsoft involving 1.89 million AI predictions, emphasizing that optimal performance requires significantly more examples (50-100) than what is commonly assumed in few-shot learning frameworks. The limitations of AI learning are highlighted, showing that while model performance improves with more data, they become less reliable when faced with varied inputs. The study concludes that current AI models excel in recognizing patterns but struggle with generalization, prompting a need for more robust strategies in AI deployment.

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Published via Towards AI