Dear all, Our next AI seminar on *"Lipstick on a Pig: Using Language Models as Few-Shot Learners" *by Sameer Singh is scheduled to be on June 9th (Friday), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.google.com%2Fcalendar%2Fevent%3Faction%3DTEMPLATE%26tmeid%3DajRoZHY0ZG40ZzQ4aWZhMTRxc2l2c2tnYzhfMjAyMzA2MDlUMjAwMDAwWiBjX25rNXRvdjk5bXZpZ2lnc3RndWEzcm11dGtzQGc%26tmsrc%3Dc_nk5tov99mvigigstgua3rmutks%2540group.calendar.google.com&data=05%7C01%7Cai%40engr.orst.edu%7Ca69bc53a3f784435978208db66c8c2d0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638216784663889792%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=9g7RTN90qTQhNniS4L9m6IyCOr8Ze0wZDy3cBsxMEK4%3D&reserved=0> ) *Location: * https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonstate.zoom.us%2Fj%2F94177772718%3Fpwd%3DY2VUK3d2Zk5HcVFZZVI3VkVlTnhkZz09&data=05%7C01%7Cai%40engr.orst.edu%7Ca69bc53a3f784435978208db66c8c2d0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638216784663889792%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=XQvSTQAmQSgqRjTrtEAEHOCuDA9pMrZjKOrY4uNnRdk%3D&reserved=0 Please note that this is a Zoom-only event *Lipstick on a Pig: Using Language Models as Few-Shot Learners* Sameer Singh Associate Professor, Computer Science University of California, Irvine *Abstract:* Today's Natural Language Processing (NLP) strategies are heavily reliant on pre-trained language models due to their ability to deliver semantically rich representations. While these models provide impressive few-shot natural language understanding and reasoning capabilities, simply using them as "fill in the blank" prompts may not be a one-size-fits-all solution. Despite the allure of direct application of these models - an allure that grows with model and dataset sizes - the objectives of language modeling and few-shot learning are not perfectly aligned. Unpacking this disparity is crucial. In this talk, I will describe some of our work in characterizing the differences between language modeling and few-shot learning. I will show how language modeling comes with crucial shortcomings for few-shot adaptation and describe a simple approach to address them. Then, focusing on numerical reasoning, I will show that the reasoning ability of the language models depends strongly on simple statistics of the pretraining corpus, performing much more accurately for more common terms. These results suggest language modeling may not be sufficient to learn robust reasoners and that we need to take the pretraining data into account when interpreting few-shot evaluation results. While language models hold substantial promise, making these 'pigs' presentable with 'lipstick' may require a more comprehensive approach than currently anticipated. *Speaker Bio:* Dr. Sameer Singh is an Associate Professor of Computer Science at the University of California, Irvine (UCI). He is working primarily on the robustness and interpretability of machine learning algorithms and models that reason with text and structure for natural language processing. Sameer was a postdoctoral researcher at the University of Washington and received his Ph.D. from the University of Massachusetts, Amherst. He has received the NSF CAREER award, UCI Distinguished Early Career Faculty award, the Hellman Faculty Fellowship, and was selected as a DARPA Riser. His group has received funding from Allen Institute for AI, Amazon, NSF, DARPA, Adobe Research, Hasso Plattner Institute, NEC, Base 11, and FICO. Sameer has published extensively at machine learning and natural language processing venues and received conference paper awards at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, ACL 2020, and NAACL 2022. (https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsameersingh.org%2F&data=05%7C01%7Cai%40engr.orst.edu%7Ca69bc53a3f784435978208db66c8c2d0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638216784663889792%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=T%2BZ6%2Fc99%2FwSCHIoYCBWj8TA8p15FwzW5Sisy1ZsHA7w%3D&reserved=0) *Please watch this space for future AI Seminars :* https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineering.oregonstate.edu%2FEECS%2Fresearch%2FAI&data=05%7C01%7Cai%40engr.orst.edu%7Ca69bc53a3f784435978208db66c8c2d0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638216784663889792%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=ICGnMdx94vn3mOnZXc53XA2VaeZeSza3ibeTl9WpFlc%3D&reserved=0 Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged