Events Calendar

NL Seminar-Mission: Impossible Language Models

Speaker: Julie Kallini, Stanford University

Talk Title: Mission: Impossible Language Models

Abstract: REMINDER: Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event  Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins. If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location. ZOOM INFO:  https://usc.zoom.us/j/97400245543?pwd=uo9TL9Ss4TA4Wa4TPtfDQnedE7Va8B.1 Meeting ID: 974 0024 5543 Passcode: 407395 Chomsky and others have very directly claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn. However, there is very little published experimental evidence to support such a claim. Here, we develop a set of synthetic impossible languages of differing complexity, each designed by systematically altering English data with unnatural word orders and grammar rules. These languages lie on an impossibility continuum: at one end are languages that are inherently impossible, such as random and irreversible shuffles of English words, and on the other, languages that may not be intuitively impossible but are often considered so in linguistics, particularly those with rules based on counting word positions. We report on a wide range of evaluations to assess the capacity of GPT-2 small models to learn these uncontroversially impossible languages, and crucially, we perform these assessments at various stages throughout training to compare the learning process for each language. Our core finding is that GPT-2 struggles to learn impossible languages when compared to English as a control, challenging the core claim. More importantly, we hope our approach opens up a productive line of inquiry in which different LLM architectures are tested on a variety of impossible languages in an effort to learn more about how LLMs can be used as tools for these cognitive and typological investigations.

Biography: Julie Kallini is a second-year Computer Science Ph.D. student at Stanford University advised by Christopher Potts and Dan Jurafsky. Her research spans several topics in natural language processing, including computational linguistics, cognitive science, interpretability, and model architecture. Julie's work is generously supported by the NSF Graduate Research Fellowship, the Stanford School of Engineering Graduate Fellowship, and the Stanford EDGE Fellowship.   Before starting her Ph.D., Julie was a software engineer at Meta, where she worked on machine learning for advertisements. Julie graduated summa cum laude from Princeton University with a B.S.E. in Computer Science and a minor in Linguistics.

Host: Jonathan May and Katy Felkner

More Info: https://www.isi.edu/research-groups-nlg/nlg-seminars/

Webcast: https://www.youtube.com/watch?v=sDMUu8rrgV8

Location: Information Science Institute (ISI) - Conf Rm#689

WebCast Link: https://www.youtube.com/watch?v=sDMUu8rrgV8

Audiences: Everyone Is Invited

Contact: Pete Zamar

Event Link: https://www.isi.edu/research-groups-nlg/nlg-seminars/