BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Researching and Revising What Language Models Say\, Using Lang uage Models 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:2023-04-24-anthony-chen@cml.ics.uci.edu DTSTAMP:20230424T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20230424T130000 DTEND;TZID=America/Los_Angeles:20230424T140000 SUMMARY:[CML Seminar] Anthony Chen: Researching and Revising What Language Models Say\, Using Language Models LOCATION:Donald Bren Hall 4011 DESCRIPTION:Anthony Chen\, PhD Student\, Department of Computer Science\, U C Irvine\n\nTitle: Researching and Revising What Language Models Say\, Usi ng Language Models\n\nAbstract: As the strengths of large language models (LLMs) have become prominent\, so too have their weaknesses. A glaring wea kness of LLMs is their penchant for generating false\, biased\, or mislead ing claims in a phenomena broadly referred to as hallucinations. Most LLMs also do not ground their generations to any source. To enable attribution while still preserving all the powerful advantages of LLMs\, we propose R ARR (Retrofit Attribution using Research and Revision)\, a system that aut omatically retrieves evidence to support the output of any LLM and then po st-edits the output to fix any information that contradicts the retrieved evidence while preserving the original output as much as possible. When ap plied to several state-of-the-art LLMs\, RARR significantly improves attri bution.\n\nhttps://cml.ics.uci.edu/seminars/2023-04-24-anthony-chen X-ALT-DESC;FMTTYPE=text/html:Anthony Chen\, PhD Student\ , Department of Computer Science\, UC Irvine

Title: Research ing and Revising What Language Models Say\, Using Language Models

< b>Abstract: As the strengths of large language models (LLMs) have beco me prominent\, so too have their weaknesses. A glaring weakness of LLMs is their penchant for generating false\, biased\, or misleading claims in a phenomena broadly referred to as hallucinations. Most LLMs also do not gro und their generations to any source. To enable attribution while still pre serving all the powerful advantages of LLMs\, we propose RARR (Retrofit At tribution using Research and Revision)\, a system that automatically retri eves evidence to support the output of any LLM and then post-edits the out put to fix any information that contradicts the retrieved evidence while p reserving the original output as much as possible. When applied to several state-of-the-art LLMs\, RARR significantly improves attribution.

< a href="https://cml.ics.uci.edu/seminars/2023-04-24-anthony-chen">https:// cml.ics.uci.edu/seminars/2023-04-24-anthony-chen URL:https://cml.ics.uci.edu/seminars/2023-04-24-anthony-chen END:VEVENT END:VCALENDAR