Structural Augmented Reasoning for Language Models

PhD Thesis Proposal Defence


Title: "Structural Augmented Reasoning for Language Models"

by

Mr. Yubo WANG


Abstract:

Large language models have achieved strong performance across a wide range of 
natural language understanding and generation tasks. However, their reasoning 
often relies on shallow pattern matching over unstructured inputs, limiting 
their effectiveness in scenarios that demand external domain knowledge, 
fine-grained entity-level understanding, or rapid adaptation to evolving task 
requirements with minimal supervision. This dissertation investigates this 
problem through the lens of structural augmented reasoning: the integration 
of explicit structured representations—such as knowledge graphs and 
dynamically constructed weighted graphs—into language model pipelines to 
enable more precise, context-aware, and adaptable reasoning.

The dissertation develops this perspective through two concrete problem 
settings. First, it addresses knowledge-intensive tabular understanding, 
where language models lack sufficient semantic context to accurately 
interpret table columns. It presents KGLink, a hybrid framework that bridges 
knowledge graph information with pre-trained language models, resolving the 
granularity mismatch between knowledge graph-derived types and 
dataset-specific labels while compensating for missing contextual cues in 
table content. Second, it tackles dynamic few-shot text classification in 
social media domains, where target labels evolve over time and labeled data 
remains scarce. It introduces GORAG, a graph-based online retrieval-augmented 
generation framework that constructs and maintains an adaptive weighted graph 
to achieve threshold-free, input-specific retrieval, ensuring that the 
contextual information provided to the language model remains both 
comprehensive and precise.

Taken together, these studies support a unified view: language models benefit 
substantially from structured intermediate representations that organize, 
filter, and prioritize external knowledge before it reaches the model. The 
central contribution of this dissertation is to demonstrate that structural 
augmentation—whether grounded in external knowledge graphs or dynamically 
constructed from task-specific signals— provides a principled and effective 
pathway for improving language model reasoning across diverse, 
knowledge-intensive applications.


Date:                   Thursday, 22 May 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 2128A
                        Lift 19

Committee Members:      Prof. Lei Chen (Supervisor)
                        Dr. Dan Xu (Chairperson)
                        Dr. Chaojian Li