4 citations · 11 across the 19 of their papers we have counts for
22 papers
An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems
Quoc Viet Nguyen, Trinh Pham, Viet Huynh +4
Recommender systems have become core infrastructure for modern online platforms, personalizing content at scale and strongly influencing what users see, click on, and purchase. How…
Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings
Yubo Wang, Shujie Cui, James Bailey +5
Dense text embeddings are widely used in data mining, retrieval, and downstream machine learning systems due to their compact and semantically rich representations, but recent embe…
Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
Trinh Pham, Viet Huynh, Hongzhi Yin +2
The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released mo…
An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data
Trinh Pham, Thanh Tam Nguyen, Viet Huynh +2
Recent advances in large language models have strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to…
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…