This AI Paper from Stanford University Evaluates the Performance of Multimodal Foundation Models Scaling from Few-Shot to Many-Shot-In-Context Learning ICL

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Summary

● Incorporating in-context learning significantly enhances large language models and multimodal models.
● Few-shot multimodal in-context learning improves performance on out-of-domain tasks.
● Increased demonstrating examples improve model performance in LLMs and LMMs.
● Gemini 1.5 Pro shows consistent performance improvements compared to GPT-4o.
● Batching queries with many-shot in-context learning reduces latency and costs without sacrificing performance.

Author: Mohammad Asjad
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