BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Effective\, explainable\, and equitable predictions in NLP mod els with world knowledge and conversations 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:2022-10-17-bodhisattwa-prasad-majumder@cml.ics.uci.edu DTSTAMP:20221017T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20221017T130000 DTEND;TZID=America/Los_Angeles:20221017T140000 SUMMARY:[CML Seminar] Bodhisattwa Prasad Majumder: Effective\, explainable\ , and equitable predictions in NLP models with world knowledge and convers ations LOCATION:Donald Bren Hall 4011 DESCRIPTION:Bodhisattwa Prasad Majumder\, PhD Student\, Department of Compu ter Science and Engineering\, University of California\, San Diego\n\nTitl e: Effective\, explainable\, and equitable predictions in NLP models with world knowledge and conversations\n\nAbstract: The use of artificial intel ligence in knowledge-seeking applications has shown remarkable effectivene ss\, but increasing demand for interaction and accessibility requires the underlying components to be grounded in up-to-date real-world context. In this talk\, I discuss methods to effectively inject up-to-date knowledge i nto an existing dialog model without additional training\, the role of bac kground knowledge in generating faithful natural language explanations\, a nd a conversational framework to address subjectivity—balancing task per formance and bias mitigation for fair interpretable predictions.\n\nhttps: //cml.ics.uci.edu/seminars/2022-10-17-bodhisattwa-prasad-majumder X-ALT-DESC;FMTTYPE=text/html:Bodhisattwa Prasad Majumder \, PhD Student\, Department of Computer Science and Engineering\, Universi ty of California\, San Diego

Title: Effective\, explainable\ , and equitable predictions in NLP models with world knowledge and convers ations

Abstract: The use of artificial intelligence in knowl edge-seeking applications has shown remarkable effectiveness\, but increas ing demand for interaction and accessibility requires the underlying compo nents to be grounded in up-to-date real-world context. In this talk\, I di scuss methods to effectively inject up-to-date knowledge into an existing dialog model without additional training\, the role of background knowledg e in generating faithful natural language explanations\, and a conversatio nal framework to address subjectivity—balancing task performance and bia s mitigation for fair interpretable predictions.

https://cm l.ics.uci.edu/seminars/2022-10-17-bodhisattwa-prasad-majumder URL:https://cml.ics.uci.edu/seminars/2022-10-17-bodhisattwa-prasad-majumder END:VEVENT END:VCALENDAR