Semantic Grounding in Large Language Models: Methods, Challenges, and Applications

PhD Qualifying Examination


Title: "Semantic Grounding in Large Language Models: Methods, Challenges, 
and Applications"

by

Mr. Jiayang CHENG


Abstract:

Semantic grounding--the process by which language models connect their 
outputs to verifiable knowledge sources and factual information--has emerged 
as a critical challenge with the advent of Large Language Models (LLMs). 
These models, while demonstrating remarkable generation capabilities across 
diverse domains, face fundamental issues with factual accuracy, 
hallucination, and lack of transparency in their reasoning processes. We 
present an in-depth analysis of core grounding tasks including knowledge 
base question answering, retrieval-augmented generation, and fact 
verification, examining the evolution from traditional symbolic systems to 
modern agentic approaches that incorporate sophisticated reasoning 
capabilities. We organize current methods across three key dimensions: 
parametric knowledge grounding, context-based grounding, and external 
knowledge grounding, analyzing how each addresses challenges such as 
hallucination mitigation and source attribution. Furthermore, we identify 
critical limitations in existing approaches and outline promising directions 
for future research to advance reliable and transparent language generation.


Date:                   Thursday, 19 June 2025

Time:                   9:00am - 11:00am

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Dr. Yangqiu Song (Supervisor)
                        Prof. Raymond Wong (Chairperson)
                        Dr. Dan Xu