This article investigates in-context learning (ICL) performance enhancements in multimodal foundation models by transitioning from few-shot to many-shot examples. Using benchmarks from GPT-4o and Gemini 1.5 Pro over ten datasets involving diverse domains, significant performance improvements were noted in using up to nearly 2,000 demonstrating examples. Additionally, the study highlights the efficiency of query batching, revealing that batching up to 50 queries optimizes performance while reducing costs and latency. This research underlines the potential of extensive ICL in advancing model capability and efficiency in varied applications.
In assessing the capabilities of multimodal foundation models, we found that scaling from few-shot to many-shot ICL significantly enhances performance across various tasks and datasets.
Our experiments demonstrated that batching multiple queries not only slashes costs and latency but also notably enriches performance in both zero-shot and many-shot scenarios.
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