微软亚洲研究院 ACE Talk 系列讲座旨在邀请杰出的学术新星分享科研成果，为学生与研究员提供相互交流学习与洞悉前沿动态的平台。

第十五期 ACE Talk，我们特别邀请到来自加利福尼亚大学圣塔芭芭拉分校的冠名讲席教授王威廉为我们带来以“Principles of Reasoning: Compositional and Collaborative Generative AI Design”为主题的报告，介绍他在 LLM 中引入 Logic-LM 框架、构建神经符号解决方案的研究工作，共同展望生成人工智能模块化和协作式的未来。

本次讲座特向北京高校的同学们开放线下参会名额，欢迎大家积极报名！

讲座信息

时间：12 月 13 日（周三）10:00 - 11:15

地点：微软亚洲研究院（北京市海淀区丹棱街 5 号）

日程：

• 10:00-11:00 嘉宾报告

• 11:00-11:15 微软亚洲研究院参观（限学生）

报名方式

欢迎扫描下方二维码填写报名问卷，报名成功后将收到邮件通知。

线下参与名额有限，请感兴趣的同学尽早报名。未获得线下参与机会的报名同学仍可以通过 Teams 会议链接远程参与讲座活动。

报名截止时间：12 月 12 日（周二）12:00

报名链接：https://jinshuju.net/f/TmpFUT

嘉宾介绍

Dr. William Wang has established a distinguished career in the field of artificial intelligence at the University of California, Santa Barbara. Since 2019, he has been the Mellichamp Professor of Artificial Intelligence, and concurrently serves as the Director of the UCSB Center for Responsible Machine Learning, UCSB Mind and Machine Intelligence Initiative, and the UCSB Natural Language Processing Group. His leadership and contributions to the field have been recognized with several prestigious awards, including the CRA Undergraduate Research Faculty Mentoring Award in 2023, the British Computer Society - Karen Spärck Jones Award in 2022, and the NSF CAREER Award in 2021. Dr. Wang was also listed among IEEE AI's 10 to Watch in 2020 and has received accolades for his research, including the CVPR Best Student Paper Award in 2019 and the DARPA Young Faculty Award in 2018.

报告简介

Principles of Reasoning: Compositional and Collaborative Generative AI Design

A majority of existing research in large language models and generative AI systems focus on scaling and engineering. In this talk, I argue that we need principled understanding of the science of generative AI, in particular, to understand the emergent ability of large language models. I present a Bayesian latent variable approach to enhancing in-context learning in large language models (LLMs) through optimal demonstration selection, demonstrating substantial improvements across various text classification tasks.

Second, I argue that modern generative AI systems must be modular and collaborative, to solve complex reasoning problems. We introduce Logic-LM, a novel framework that synergizes LLMs with symbolic solvers, significantly boosting logical problem-solving abilities. We will also elaborate how to build neuro-symbolic solutions to improve the compositionality in text-to-image systems.

Our observations indicate that the future of generative AI is modular and collaborative, as opposed to a single-model system.

主持人简介

Dr. Nan Duan is a senior principal researcher and research manager of the Natural Language Computing group at Microsoft Research Asia. He is an adjunct Ph.D. supervisor at University of Science and Technology of China and Xi’an Jiaotong University, and an adjunct professor at Tianjin University. His research interests include natural language processing, multimodal foundation model, code intelligence, and machine reasoning. He served as the program chair and area chair at NLP/AI conferences. He published 100+ research papers with 10000+ Google Scholar citations and holds 20+ patents. He was awarded as Distinguished Member of China Computer Federation (CCF), CCF-NLPCC Distinguished Young Scientist (2019), DeepTech Intelligent Computing Innovators China (2022).

关于ACE Talk

ACE (Accelerate, Create, Empower) Talk Series epitomizes our commitment across three dimensions. To accelerate the swift adoption of cutting-edge research where researchers and students can share the latest breakthroughs and advancements. To create an environment that nurtures novel ideas and fosters the genesis of innovative solutions to complex problems. At its core, we are dedicated to empowering individuals, including our speakers and audiences to drive positive changes on a broader scale. Through this endeavor, we aspire to enhance global communication and cultivate a diverse academic atmosphere, connecting talented individuals worldwide and ultimately contributing to meaningful change within the academic research community