The study evaluates many-shot in-context learning (ICL) of multimodal foundation models across 10 datasets, revealing performance improvements and benefits of batching queries. Many-shot ICL allows significant adaptability for users, enabling faster results without the need for fine-tuning. It highlights the potential for these models to handle large demonstrating examples while addressing performance and cost efficiency. Despite the promise of ICL, the study emphasizes the need for further exploration of its comparison with traditional fine-tuning methods, alongside addressing issues like model biases and inaccuracies.
Our findings suggest that these multimodal foundation models have the capability of performing ICL with large numbers of demonstrating examples, which may have significant implications on their practical use.
One significant advantage of many-shot ICL is its ability to get quick results even on the same day of model release, and that's why we can finish our evaluation using GPT-4o within days.
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