M365 Research
Pioneering advancements in AI and Systems
News & features
TaskWeaver: A code-first agent framework for efficient data analytics and domain adaptation
| Shilin He, Liqun Li, Xu Zhang, Bo Qiao, Chaoyun Zhang, Yu Kang, Rujia Wang, Qingwei Lin 林庆维, Saravan Rajmohan, and Dongmei Zhang
AI-backed virtual assistants face challenges in handling complex data structures. TaskWeaver helps users build assistants that understand diverse domain questions, follow examples, and efficiently execute customizable algorithms on complex data structures.
LLMLingua: Innovating LLM efficiency with prompt compression
| Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu
Advanced prompting technologies for LLMs can lead to excessively long prompts, causing issues. Learn how LLMLingua compresses prompts up to 20x, maintaining quality, reducing latency, and supporting improved UX.
Rethinking trust in direct messages in the AI era
| Kim Laine, Shrey Jain, Betül Durak, Radames Cruz Moreno, and Robert Sim
Microsoft researchers are proposing a new way to ensure greater trust and accountability in email, texts, direct messages on social platforms, even phone calls, to help mitigate sophisticated threats from AI-related scams and fraud.
Large-language models for automatic cloud incident management
| Rujia Wang, Chetan Bansal, Supriyo GHOSH, Tom Zimmermann, Xuchao Zhang, and Saravan Rajmohan
This research was accepted by the IEEE/ACM International Conference on Software Engineering (ICSE) (opens in new tab), which is a forum for researchers, practitioners, and educators to gather, present, and discuss the most recent innovations, trends, experiences, and issues in…
FLUTE: A scalable federated learning simulation platform
| Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Eduardo Madrigal Diaz, Andre Manoel, and Robert Sim
Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However,…
Privacy Preserving Machine Learning: Maintaining confidentiality and preserving trust
| Victor Ruehle, Robert Sim, Sergey Yekhanin, Nishanth Chandran, Melissa Chase, Daniel Jones, Kim Laine, Boris Köpf, Jaime Teevan, Jim Kleewein, and Saravan Rajmohan
Machine learning (ML) offers tremendous opportunities to increase productivity. However, ML systems are only as good as the quality of the data that informs the training of ML models. And training ML models requires a significant amount of data, more…