BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250409T120000
DTEND;TZID=America/New_York:20250409T131500
DTSTAMP:20260602T104724
CREATED:20250402T130550Z
LAST-MODIFIED:20250402T130550Z
UID:13798-1744200000-1744204500@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Alignment and Control with Representation Engineering"
DESCRIPTION:Abstract: \nLarge Language Models (LLMs) are vulnerable to adversarial attacks\, which bypass common safeguards put in place to prevent these models from generating harmful output. Notably\, these attacks can be transferrable to other models—even proprietary ones—potentially compromising a wide range of AI systems with a single exploit. This surprising fragility underscores a critical weakness in current AI safeguards. \nIn this talk\, we illustrate how these attacks are discovered\, and several recent advances that take advantage of models’ internal representations to thwart them. Unlike much prior work that relies on adversarial training methods\, this approach directly controls neural representations responsible for harmful and unwanted behaviors\, while remaining agnostic to particular attacks. Notably\, in start contrast with prior work we show that these methods can remain effective while preserving the model’s performance on non-adversarial inputs. Our findings suggest that achieving robust safety in generative models may be an attainable goal. \nZoom Link:https://upenn.zoom.us/j/95869536469
URL:https://seasevents.nmsdev7.com/event/asset-seminar-alignment-and-control-with-representation-engineering/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
END:VEVENT
END:VCALENDAR