The article explores the performance of Gemini 1.5 Pro and GPT-4o in many-shot in-context learning (ICL) with batched queries. It highlights consistent accuracy improvements as the number of demonstrating examples rises, particularly on datasets like HAM10000 and EuroSAT. However, there are exceptions; some datasets do not benefit beyond a certain point, emphasizing the complexity of optimizing example selection. Additionally, the article discusses cost and latency implications of batching queries, providing a comprehensive look at the methodologies employed and the results achieved across various datasets.
Gemini 1.5 Pro displays substantial accuracy gains with increasing demonstrating examples, achieving notable improvements across various datasets, especially in medical QA tasks.
While many-shot ICL generally boosts performance, some datasets show diminishing returns beyond an optimal number of demo examples, highlighting the need for careful selection.
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