12 citations · 16 across the 5 of their papers we have counts for
5 papers
FINER-SQL: Boosting Small Language Models for Text-to-SQL
Thanh Dat Hoang, Thanh Trung Huynh, Matthias Weidlich +4
Large language models have driven major advances in Text-to-SQL generation. However, they suffer from high computational cost, long latency, and data privacy concerns, which make t…
A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback
Thanh Dat Hoang, Thanh Trung Huynh, Matthias Weidlich +4
Text2SQL, the task of generating SQL queries from natural language text, is a critical challenge in data engineering. Recently, Large Language Models (LLMs) have demonstrated super…
Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers
Thanh Dat Hoang, Thanh Tam Nguyen, Thanh Trung Huynh +2
Most modern Text2SQL systems prompt large language models (LLMs) with entire schemas -- mostly column information -- alongside the user's question. While effective on small databas…
On Node Features for Graph Neural Networks
Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang +2
Graph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning pr…
Parallel Computation of Graph Embeddings
Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang +4
Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph propert…