The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)

Speaker: Dr. Xin Luna Dong
         Principal Scientist
         Meta Reality Labs

Title:   "The Journey to A Knowledgeable Assistant with Retrieval-Augmented
         Generation (RAG)"

Date:    Monday, 5 August 2024

Time:    11:00am - 12 noon

Venue:   Lecture Theater F
         (Leung Yat Sing Lecture Theater), via lift 25/26, HKUST

Abstract:

For decades, multiple communities (Database, Information Retrieval,
Natural Language Processing, Data Mining, AI) have pursued the mission of
providing the right information at the right time. Efforts span web
search, data integration, knowledge graphs, question answering. Recent
advancements in Large Language Models (LLMs) have demonstrated remarkable
capabilities in comprehending and generating human language,
revolutionizing techniques in every front. However, their inherent
limitations such as factual inaccuracies and hallucinations make LLMs less
suitable for creating knowledgeable and trustworthy assistants.

This talk describes our journey in building a knowledgeable AI assistant
by harnessing LLM techniques. We start with our findings from a
comprehensive set of experiments to assess LLM reliability in answering
factual questions and analyze performance variations across different
knowledge types. Next, we describe our federated Retrieval-Augmented
Generation (RAG) system that integrates external information from both the
web and knowledge graphs for trustworthy text generation on real-time
topics like stocks and sports, as well as on torso-to-tail entities like
local restaurants. Additionally, we brief our explorations on extending
our techniques towards multi-modal, contextualized, and personalized Q\&A.
We will share our techniques, our findings, and the path forward,
highlighting how we are leveraging and advancing the decades of work in
this area.

Biography:

Xin Luna Dong is a Principal Scientist at Meta Reality Labs, leading the
ML efforts in building an intelligent personal assistant. She has spent
more than a decade building knowledge graphs, such as the Amazon Product
Graph and the Google Knowledge Graph. She has co-authored books "Machine
Knowledge: Creation and Curation of Comprehensive Knowledge Bases" and
"Big Data Integration". She was named an ACM Fellow and an IEEE Fellow for
"significant contributions to knowledge graph construction and data
integration", awarded the VLDB Women in Database Research Award and VLDB
Early Career Research Contribution Award. She serves in the PVLDB advisory
committee, was a member of the VLDB endowment, a PC co-chair for KDD'2022
ADS track, WSDM'2022, VLDB'2021, and Sigmod'2018.