BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Steering Textual Reasoning with Explanations 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:2024-03-14-xi-ye@cml.ics.uci.edu DTSTAMP:20240314T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20240314T110000 DTEND;TZID=America/Los_Angeles:20240314T120000 SUMMARY:[CML Seminar] Xi Ye: Steering Textual Reasoning with Explanations LOCATION:Donald Bren Hall 4011 DESCRIPTION:Xi Ye\, PhD Student\, Department of Computer Science\, Universi ty of Texas at Austin\n\nTitle: Steering Textual Reasoning with Explanatio ns\n\nAbstract: Large language models (LLMs) have significantly extended t he boundaries of NLP's potential applications\, partially because of their increased ability to do complex reasoning. However\, LLMs have well-docum ented reasoning failures\, such as hallucinations and inability to systema tically generalize. In this talk\, I describe my work on enhancing LLMs in reliably performing textual reasoning\, with a particular focus on levera ging explanations. I will first introduce a framework for automatically as sessing the robustness of black-box models using explanations. I will then describe how to form effective explanations for better teaching LLMs to r eason. My work uses declarative formal specifications as explanations\, wh ich enables using an SMT solver to amend the limited planning capabilities of LLMs. Finally\, I will describe future directions for further enhancin g LLMs to better aid humans in challenging real-world applications demandi ng deep reasoning.\n\nhttps://cml.ics.uci.edu/seminars/2024-03-14-xi-ye X-ALT-DESC;FMTTYPE=text/html:Xi Ye\, PhD Student\, Depar tment of Computer Science\, University of Texas at Austin

Title: Steering Textual Reasoning with Explanations

Abstract: Large language models (LLMs) have significantly extended the boundaries of NLP's potential applications\, partially because of their increased abili ty to do complex reasoning. However\, LLMs have well-documented reasoning failures\, such as hallucinations and inability to systematically generali ze. In this talk\, I describe my work on enhancing LLMs in reliably perfor ming textual reasoning\, with a particular focus on leveraging explanation s. I will first introduce a framework for automatically assessing the robu stness of black-box models using explanations. I will then describe how to form effective explanations for better teaching LLMs to reason. My work u ses declarative formal specifications as explanations\, which enables usin g an SMT solver to amend the limited planning capabilities of LLMs. Finall y\, I will describe future directions for further enhancing LLMs to better aid humans in challenging real-world applications demanding deep reasonin g.

http s://cml.ics.uci.edu/seminars/2024-03-14-xi-ye URL:https://cml.ics.uci.edu/seminars/2024-03-14-xi-ye END:VEVENT END:VCALENDAR