Retrieval-augmented generation (RAG) allows AI systems to provide additional information and context to a large language model (LLM) when generating a response to a user query. However, traditional RAG-based methods  

Our work on Orca and Orca 2 demonstrated the power of using synthetic data for the post-training of small language models and getting them to levels of performance previously found only in much larger language models. Orca-AgentInstruct is another step in this direction, where we explore using agentic flows to generate diverse and high-quality data at scale. Orca-AgentInstruct is an agentic solution for synthetic-data generation. By leveraging an agentic framework, AgentInstruct can generate tailored datasets, comprising both prompts and responses, […]

Today, development of generalizable AI models requires access to sufficient data and compute resources, which may create challenges for some researchers. Democratizing access to technology across the research community can advance the development of generalizable AI models. By applying the core software development concept of modularity to AI, we can build models that are powerful, efficient, adaptable, and transparent.  Until recently, AI models were primarily built using monolithic architecture. Though powerful, these models can be challenging to customize and edit […]

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. NEW RESEARCH Look Ma, no markers: holistic performance capture without the hassle Motion-capture technologies used in film and game production typically focus solely on face, body, or hand capture, requiring complex and expensive hardware and lots of manual intervention from skilled operators. While machine-learning-based