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Advances in run-time strategies for next-generation foundation models

A visual illustration of Medprompt performance on the MedQA benchmark. Moving from left to right on a horizontal line, the illustration shows how different Medprompt components and additive contributions improve accuracy starting with zero-shot at 81.7 accuracy, to random few-shot at 83.9 accuracy, to random few-shot, chain-of-thought at 87.3 accuracy, to kNN, few-shot, chain-of-thought at 88.4 accuracy, to ensemble with choice shuffle at 90.2 accuracy.
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Microsoft Research Blog

MarS: A unified financial market simulation engine in the era of generative foundation models 

December 4, 2024

Research Focus: Week of December 2, 2024
Microsoft Research Blog

Research Focus: Week of December 2, 2024 

December 4, 2024

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Microsoft Research Blog

Accelerating drug discovery with TamGen: A generative AI approach to target-aware molecule generation 

November 25, 2024

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Microsoft Research Forum 

Research Forum | Episode 4 Keynote | Jianfeng Gao

Keynote: Phi-3-Vision: A highly capable and “small” language vision model Globally Equitable AI 

Research Forum | Episode 4 Panel | John Langford, Hoifung Poon, Katja Hofmann, Jianwei Yang

Panel Discussion: Beyond Language: The future of multimodal models in healthcare, gaming, and AI 

Research Forum | Episode 4 - abstract chalkboard background with colorful network nodes and circular icons

Research Forum Brief | September 2024 

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