How Many Examples Does AI Really Need? New Research Reveals Surprising Scaling Laws

Gemini 1.5 Pro and GPT-4o show significant performance improvements using many-shot in-context learning (ICL) across various datasets. Increasing the number of demonstrating examples generally leads to better results, with Gemini 1.5 Pro showing more consistent improvements.

Batching queries in a single prompt reduces costs and latency without substantial performance loss. Many-shot ICL with batching can significantly improve accuracy while decreasing per-query costs and processing times, making it a promising approach for enhancing large language model performance efficiently.