BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Large Language Models as External Knowledge Sources for Sequen tial Decision Making 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:2023-02-06-kolby-nottingham@cml.ics.uci.edu DTSTAMP:20230206T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20230206T130000 DTEND;TZID=America/Los_Angeles:20230206T140000 SUMMARY:[CML Seminar] Kolby Nottingham: Large Language Models as External K nowledge Sources for Sequential Decision Making LOCATION:Donald Bren Hall 4011 DESCRIPTION:Kolby Nottingham\, PhD Student\, Department of Computer Science \, University of California\, Irvine\n\nTitle: Large Language Models as Ex ternal Knowledge Sources for Sequential Decision Making\n\nAbstract: While it's common for other machine learning modalities to benefit from model p retraining\, reinforcement learning (RL) agents still typically learn tabu la rasa. Large language models (LLMs)\, trained on internet text\, have be en used as external knowledge sources for RL\, but on their own they are n oisy and lack the grounding necessary to reason in interactive environment s. In this talk\, we will cover methods for grounding LLMs in environment dynamics and applying extracted knowledge to training RL agents. Finally\, we will demonstrate our newly proposed method for applying LLMs to improv ing RL sample efficiency through guided exploration.\n\nhttps://cml.ics.uc i.edu/seminars/2023-02-06-kolby-nottingham X-ALT-DESC;FMTTYPE=text/html:Kolby Nottingham\, PhD Stud ent\, Department of Computer Science\, University of California\, Irvine
Title: Large Language Models as External Knowledge Sources fo r Sequential Decision Making

Abstract: While it's common for other machine learning modalities to benefit from model pretraining\, rei nforcement learning (RL) agents still typically learn tabula rasa. Large l anguage models (LLMs)\, trained on internet text\, have been used as exter nal knowledge sources for RL\, but on their own they are noisy and lack th e grounding necessary to reason in interactive environments. In this talk\ , we will cover methods for grounding LLMs in environment dynamics and app lying extracted knowledge to training RL agents. Finally\, we will demonst rate our newly proposed method for applying LLMs to improving RL sample ef ficiency through guided exploration.

https://cml.ics.uci.edu/seminars/ 2023-02-06-kolby-nottingham URL:https://cml.ics.uci.edu/seminars/2023-02-06-kolby-nottingham END:VEVENT END:VCALENDAR