Seminars
Abstract
Model robustness and spurious correlations have received increasing attention in the NLP community, both in methods and evaluation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model, as judged by domain experts.
When designing mitigation algorithms, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contrast, they carry important information that’s needed to make predictions by humans. In this talk, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation, learning, and evaluation.
Biography
He He is an Assistant Professor in the Department of Computer Science and the Center for Data Science at New York University. She obtained her PhD in Computer Science at the University of Maryland, College Park. Before joining NYU, she spent a year at AWS AI and was a post-doc at Stanford University before that. She is interested in building robust and trustworthy NLP systems in human-centered settings. Her recent research focus includes robust language understanding, collaborative text generation, and understanding capabilities and issues of large language models.
Abstract
Large language models (LLMs) have demonstrated incredible power, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation, we will address two fundamental questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarking techniques designed for text and models, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness of watermark detection, along with robustness against evasion attacks. Furthermore, we will showcase empirical evidence validating their effectiveness. These findings establish a solid technical groundwork for policymakers, legal professionals, and generative AI practitioners alike.
Biography
Lei Li is an Assistant Professor in Language Technology Institute at Carnegie Mellon University. He received Ph.D. from Carnegie Mellon University School of Computer Science. He is a recipient of ACL 2021 Best Paper Award, CCF Young Elite Award in 2019, CCF distinguished speaker in 2017, Wu Wen-tsün AI prize in 2017, and 2012 ACM SIGKDD dissertation award (runner-up), and is recognized as Notable Area Chair of ICLR 2023. Previously, he was a faculty member at UC Santa Barbara. Prior to that, he founded ByteDance AI Lab in 2016 and led its research in NLP, ML, Robotics, and Drug Discovery. He launched ByteDance’s machine translation system VolcTrans and AI writing system Xiaomingbot, serving one billion users.
Abstract
The growing power in computing and AI promises a near-term future of human-machine teamwork. In this talk, I will present my research group’s efforts in understanding the complex dynamics of human-machine interaction and designing intelligent machines aimed to assist and collaborate with people. I will focus on 1) tools for onboarding machine teammates and authoring machine assistance, 2) methods for detecting, and broadly managing, errors in collaboration, and 3) building blocks of knowledge needed to enable ad hoc human-machine teamwork. I will also highlight our recent work on designing assistive, collaborative machines to support older adults aging in place.
Biography
Chien-Ming Huang is the John C. Malone Assistant Professor in the Department of Computer Science at the Johns Hopkins University. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier venues in HRI, HCI, and robotics including Science Robotics, HRI, CHI, and CSCW. His research has received media coverage from MIT Technology Review, Tech Insider, and Science Nation. Huang completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www.cs.jhu.edu/~cmhuang/
