[MUSIC FADES] I’m your guest host, Ginny Badanes, and I lead Microsoft’s Democracy Forward program, where we’ve spent the past year deeply engaged in supporting democratic elections around the world, including the recent US elections. We have been working on everything from raising awareness of nation-state propaganda efforts to helping campaigns and election officials prepare for deepfakes to protecting political campaigns from cyberattacks. Today, I’m joined by two researchers who have also been diving deep into the impact of generative […]

The challenge of effective prompting AI is reshaping industries—from education to healthcare—thanks to advancements in large language models (LLMs). These models rely on prompts, carefully crafted inputs that guide them to produce relevant and meaningful outputs. While the impact of prompts is profound, creating prompts that can help with complex tasks is a time-intensive and expertise-heavy  

JINDONG WANG: Thank you. Nice to be here. STEVEN EUIJONG WHANG: It’s great to be here. HUIZINGA: So, Jindong, I’ll start with you. In just a few sentences, tell us what problem your research addresses and why people should care about it. JINDONG WANG: OK, everybody knows that with the widespread usage of large language models, hallucination has become a crucial factor of concern. Hallucination occurs when models generate false or nonexistent information. In particular, factual hallucination greatly undermines the […]

WEIZHU CHEN: Thank you for having me, Amber.  TINGLE: So let’s start with a brief overview of your paper. In a couple sentences, tell us about the problem your research addresses and, more importantly, why the research community and beyond should know about this work.  CHEN: So my team basically in Microsoft GenAI, we are working on model training. So one of the things actually we do in the pretraining, we realize the importance of the data. And we found […]

DYLAN FOSTER: Thanks for having me. TINGLE: Let’s start with a brief overview of this paper. Tell us about the problem this work addresses and why the research community should know about it. FOSTER: So this is a, kind of, a theoretical work on reinforcement learning, or RL. When I say reinforcement learning, broadly speaking, this is talking about the question of how can we design AI agents that are capable of, like, interacting with unknown environments and learning how […]

PRANJAL CHITALE: Hi, Gretchen. Thanks for having me. HUIZINGA: So, Pranjal, give us an overview of this paper. In a couple sentences, what problem are you trying to solve, and why should people care about it? CHITALE: So we are witnessing some exciting times as LLMs are rapidly evolving as tools for countless use cases. While most of these LLMs were initially leveraged for natural language processing tasks, they are now expanded across languages and modalities. However, a major gap […]

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 Adaptive Security, Erasures, and Network Assumptions in Communication-Local MPC n-party Multi-Party Computation (MPC) is a cryptographic protocol technique that allows separate parties to securely compute a function on their joint data while keeping their inputs private. To build such a protocol,  

Introduction Generative foundation models have transformed various domains, creating new paradigms for content generation. Integrating these models with domain-specific data enables industry-specific applications. Microsoft Research has used this approach to develop the large market model (LMM) and the Financial Market Simulation Engine (MarS) for the financial domain. These innovations have the potential to empower financial researchers to customize generative models for diverse scenarios, establishing a new paradigm for applying generative models to downstream tasks in financial markets. This integration may […]

Groundbreaking advancements in frontier language models are progressing rapidly, paving the way for boosts in accuracy and reliability of generalist models, making them highly effective in specialized domains. As  

The Global Health Drug Discovery Institute (opens in new tab) (GHDDI) and Microsoft Research have reached a milestone in tuberculosis (TB) drug research with TamGen (opens in new tab), an open-source (opens in new tab), transformer-based chemical language model for developing target-specific drug compounds. Working in collaboration, the joint team successfully identified several promising inhibitors for a TB protease, with the most