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DTSTART:20260329T020000
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DTSTAMP:20251203T144650Z
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DTSTART;TZID=Europe/London:20251203T133000
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DESCRIPTION:Retrieval augmented generation (RAG) with Large Language Models
  (LLMs) has attracted wide attention\, as its strengths for addressing the
  issues of LLMs such as failure to support changes\, hallucination\, short
 age of explanation\, etc. The report will introduce some recent works on u
 sing tables and knowledge graphs as sources for supporting RAG for questio
 n answering (QA)\, including effective methods based on structured data re
 writing\, and novel resources for benchmarking knowledge incompleteness\, 
 knowledge poisoning\, and attributed QA.\n\nCo-sponsored by: Ulster Univer
 sity\n\nSpeaker(s): Jiaoyan\, \n\nVirtual: https://events.vtools.ieee.org/
 m/505630
LOCATION:Virtual: https://events.vtools.ieee.org/m/505630
ORGANIZER:h.zheng@ulster.ac.uk
SEQUENCE:11
SUMMARY:Table and Knowledge Graph Augmented Question Answering with Large L
 anguage Models
URL;VALUE=URI:https://events.vtools.ieee.org/m/505630
X-ALT-DESC:Description: <br /><p style="text-align: justify\;"><span style=
 "color: rgb(51\, 51\, 51)\; font-family: Garamond\, Helvetica\, serif\; fo
 nt-size: 18.75px\; font-style: normal\; font-variant-ligatures: normal\; f
 ont-variant-caps: normal\; font-weight: 300\; letter-spacing: normal\; orp
 hans: 2\; text-align: start\; text-indent: 0px\; text-transform: none\; wi
 dows: 2\; word-spacing: 0px\; -webkit-text-stroke-width: 0px\; white-space
 : normal\; text-decoration-thickness: initial\; text-decoration-style: ini
 tial\; text-decoration-color: initial\; display: inline !important\; float
 : none\;">Retrieval augmented generation (RAG) with Large Language Models 
 (LLMs) has attracted wide attention\, as its strengths for addressing the 
 issues of LLMs such as failure to support changes\, hallucination\, shorta
 ge of explanation\, etc. The report will introduce some recent works on us
 ing tables and knowledge graphs as sources for supporting RAG for question
  answering (QA)\, including effective methods based on structured data rew
 riting\, and novel resources for benchmarking knowledge incompleteness\, k
 nowledge poisoning\, and attributed QA.</span></p>
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