BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Demystifying the Inner Workings of Language Models X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2025-04-01-sarah-wiegreffe@cml.ics.uci.edu DTSTAMP:20250401T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20250401T110000 DTEND;TZID=America/Los_Angeles:20250401T120000 SUMMARY:[CML Seminar] Sarah Wiegreffe: Demystifying the Inner Workings of L anguage Models LOCATION:Donald Bren Hall 4011 DESCRIPTION:Sarah Wiegreffe\, Postdoctoral Researcher\, Allen Institute for AI and University of Washington\n\nTitle: Demystifying the Inner Workings of Language Models\n\nAbstract: Large language models (LLMs) power a rapi dly-growing and increasingly impactful suite of AI technologies. However\, due to their scale and complexity\, we lack a fundamental scientific unde rstanding of much of LLMs' behavior\, even when they are open source. The black-box nature of LMs not only complicates model debugging and evaluatio n\, but also limits trust and usability. In this talk\, I will describe ho w my research on interpretability (i.e.\, understanding models' inner work ings) has answered key scientific questions about how models operate. I wi ll then demonstrate how deeper insights into LLMs' behavior enable both 1) targeted performance improvements and 2) the production of transparent\, trustworthy explanations for human users.\n\nhttps://cml.ics.uci.edu/semin ars/2025-04-01-sarah-wiegreffe X-ALT-DESC;FMTTYPE=text/html:Sarah Wiegreffe\, Postdocto ral Researcher\, Allen Institute for AI and University of Washington
Title: Demystifying the Inner Workings of Language Models

Abstract: Large language models (LLMs) power a rapidly-growing and increasingly impactful suite of AI technologies. However\, due to their sc ale and complexity\, we lack a fundamental scientific understanding of muc h of LLMs' behavior\, even when they are open source. The black-box nature of LMs not only complicates model debugging and evaluation\, but also lim its trust and usability. In this talk\, I will describe how my research on interpretability (i.e.\, understanding models' inner workings) has answer ed key scientific questions about how models operate. I will then demonstr ate how deeper insights into LLMs' behavior enable both 1) targeted perfor mance improvements and 2) the production of transparent\, trustworthy expl anations for human users.

https://cml.ics.uci.edu/seminars/2025-04-01-s arah-wiegreffe URL:https://cml.ics.uci.edu/seminars/2025-04-01-sarah-wiegreffe END:VEVENT END:VCALENDAR