M365 Research

LLM-based agents usually have challenges in reliably executing multi-step tasks even with detailed instructions. This is because of the error propagation nature of step-by-step agent models. We have developed FLASH agent to address these challenges through innovative reasoning and reflection…

Making Azure’s big bet possible Recent innovations in generative large language models (LLMs) have made their applications and use-cases ubiquitous. This has led to large-scale deployments of these models, using complex, expensive, and power-hungry AI accelerators, most commonly GPUs. These…

Training transformer models with differential privacy Transformer models have recently taken the field of Natural Language Processing (NLP) by storm as large language models based on the transformer architecture have shown impressive performance across a wide range of applications. However,…

In the past fifteen years, the most significant paradigm shift in the computing industry is the migration to cloud computing, which brings unprecedented opportunities of digital transformation to business, society, and human life. The implication of this is profound. It…

Consider the following scenario: Two hospitals, each having sensitive patient data, must compute statistical information about their joint data. Or, one of the hospitals has a pre-trained ML model based on sensitive patient data and another hospital either wants to…