Data Matching in the Era of Language Models: From Data Preparation to Data Access

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Data Matching in the Era of Language Models: From Data Preparation to 
Data Access"

By

Miss Shiwen WU


Abstract:

Data matching, the task of aligning records, text fragments, or query intents, 
constitutes a foundational imperative of modern data management and AI 
infrastructure. While Language Models (LMs) and Large Language Models (LLMs) 
have substantially advanced semantic matching capabilities, their practical 
deployment is severely hindered by prohibitive annotation barriers under 
low-resource regimes, task performance instability, and excessive inference 
latency. To overcome these limitations, this thesis presents 
resource-efficient and robust data matching frameworks tailored to backend 
data preparation and frontend data access.

For backend entity resolution, we first propose CLER, an end-to-end 
co-learning framework that exploits the intrinsic, complementary signals 
between the blocker and the matcher. Through confidence-aware pseudo-labeling, 
transitivity refinement, asymmetric selection, and dynamic sample 
re-weighting, CLER mitigates label noise and breaks annotation bottlenecks 
without relying on external knowledge. To further harness foundation models 
cost-effectively during data preparation, we introduce SAiLER, a framework 
that selectively invokes LLMs as annotators only for candidate pairs 
exhibiting model disagreements or logical transitivity violations. By coupling 
transitivity-guided human error correction with noise-tolerant loss weighting, 
SAiLER matches fully supervised accuracy with minimal human labeling.

For frontend interactive data access, we address the recall bottleneck of the 
recall-then-verify paradigm by designing TyRef, an on-demand, type-specific 
query reformulation framework. TyRef diagnoses semantic discrepancies into 
synonymy, conceptual hierarchies, and format variations, dynamically grounding 
expansions via verified table feedback while employing adaptive triggering and 
early stopping to preserve sub-second latency. Finally, we translate this 
algorithmic framework into FilterPilot, an interactive demonstration system 
that enables real-time inspection, latency budgeting, and human-in-the-loop 
steering. Together, these contributions bridge the gap between foundation 
models and production systems, delivering scalable, robust, and cost-effective 
data matching across the data lifecycle.


Date:                   Monday, 21 September 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Stuart Arthur GIETEL-BASTEN (SOSC)

Committee Members:      Prof. Xiaofang ZHOU (Supervisor)
                        Prof. Raymond WONG
                        Dr. Wilfred NG
                        Dr. Jia LIU (MARK)
                        Prof. Wenjie ZHANG (UNSW)