BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Fill in the ___: Prompt-Based Solutions for NLP 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:2021-03-01-robert-logan@cml.ics.uci.edu DTSTAMP:20210301T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20210301T130000 DTEND;TZID=America/Los_Angeles:20210301T140000 SUMMARY:[CML Seminar] Robert Logan: Fill in the ___: Prompt-Based Solutions for NLP LOCATION:Online (live stream) DESCRIPTION:Robert Logan\, PhD Student\, Department of Computer Science\, U niversity of California\, Irvine\n\nTitle: Fill in the ___: Prompt-Based S olutions for NLP\n\nAbstract: Recent progress in NLP has been driven by la rge neural language models (e.g.\, GPT-2 and BERT) that are pretrained usi ng self-supervised learning before being finetuned to downstream tasks. In this talk\, we describe the technique of prompting\, which reformulates t asks as fill-in-the-blank questions. We show how prompts can measure the f actual\, linguistic\, and task-specific knowledge contained in language mo dels\, introduce an approach for automatically constructing prompts via gr adient-guided search\, and cover ongoing work investigating whether prompt ing can replace finetuning — with early results showing prompting can be more effective in few-shot scenarios while being substantially more param eter efficient.\n\nhttps://cml.ics.uci.edu/seminars/2021-03-01-robert-loga n X-ALT-DESC;FMTTYPE=text/html:Robert Logan\, PhD Student\ , Department of Computer Science\, University of California\, Irvine
Title: Fill in the ___: Prompt-Based Solutions for NLP

Abstract: Recent progress in NLP has been driven by large neural langu age models (e.g.\, GPT-2 and BERT) that are pretrained using self-supervis ed learning before being finetuned to downstream tasks. In this talk\, we describe the technique of prompting\, which reformulates tasks as fill-in- the-blank questions. We show how prompts can measure the factual\, linguis tic\, and task-specific knowledge contained in language models\, introduce an approach for automatically constructing prompts via gradient-guided se arch\, and cover ongoing work investigating whether prompting can replace finetuning — with early results showing prompting can be more effective in few-shot scenarios while being substantially more parameter efficient.< br>
https://cml.ics.uci.edu/seminars/2021-03-01-robert-logan URL:https://cml.ics.uci.edu/seminars/2021-03-01-robert-logan END:VEVENT END:VCALENDAR