Li Lyna Zhang
Principal Researcher
News & features
New methods boost reasoning in small and large language models
| Li Lyna Zhang, Xian Zhang, Xueting Han, and Dongdong Zhang
New techniques are reimagining how LLMs reason. By combining symbolic logic, mathematical rigor, and adaptive planning, these methods enable models to tackle complex, real-world problems across a variety of fields.
In the news | The Sequence
Making Small Models Great Achieve GPT-o1 Levels in Math Reasoning with Microsoft rStar-Math
rStar-Math is a novel approach that significantly boosts the mathematical reasoning capabilities of small language models (SLMs). This innovative system enables SLMs to achieve performance levels comparable to, and even exceeding, OpenAI’s o1, despite a significantly smaller model size. This…
Abstracts: July 29, 2024
| Gretchen Huizinga and Li Lyna Zhang
A lack of appropriate data, decreased model performance, and other obstacles have made it difficult to expand the input language models can receive. Li Lyna Zhang introduces LongRoPE, a method capable of extending content windows to more than 2 million…
Research Focus: Week of March 18, 2024
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. Large language models (LLMs) have shown impressive capabilities, yet they still struggle with…
Efficient and hardware-friendly neural architecture search with SpaceEvo
| Li Lyna Zhang, Jiahang Xu, Quanlu Zhang, Yuqing Yang, Ting Cao, and Mao Yang
A persistent challenge in deep learning is optimizing neural network models for diverse hardware configurations, balancing performance and low latency. Learn how SpaceEvo automates hardware-aware neural architecture search to fine-tune DNN models for swift execution on diverse devices.
Awards | The 19th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2021) | June 2021
Li Lyna Zhang, Ting Cao, and Yuqing Yang Mobisys 2021 Best Paper Award
Li Lyna Zhang, Ting Cao, and Yuqing Yang Mobisys 2021 Best Paper Award nn-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices.