Xiao HUANG

Assistant Professor Department of Computing HK Polytechnic University

I am leading the DEEP Lab at The Hong Kong Polytechnic University. We strive to develop effective and efficient large language model (LLM)-based algorithms to solve real-world prediction problems in code generation, education, recommender systems, biochemistry, etc. Our research interests span LLMs, knowledge graphs, retrieval-augmented generation, question answering, recommender systems, text-to-SQL, and graph neural networks. Open positions: I am actively recruiting Ph.D. students and postdocs (fully funded). Please feel free to drop me emails with your CV. Self-funded visiting students/scholars are welcome.

News and Highlights

• [2024/07] You are welcome to attend our PhD Symposium at ICDE 2025

• [2024/07] We released a survey of LLM-based text-to-SQL

• [2024/05] We have six papers accepted by ICML 2024, ACL 2024, and KDD 2024
  

• [2024/04] We have three papers accepted by IJCAI 2024
  

• [2023/07] Our paper won Best Paper Award Honorable Mention at SIGIR 2023
  

• [2022/07] Our ECS project, Error-Aware and Contrastive Knowledge Graph Learning, has been funded
  

Selected Publications (16 out of 83)

Text-to-SQL based on LLMs

1. Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL, Zijin Hong, Zheng Yuan, Qinggang Zhang, Hao Chen, Junnan Dong, Feiran Huang, Xiao Huang, arXiv.2406.08426, 2024.

2. Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM, Zijin Hong, Zheng Yuan, Hao Chen, Qinggang Zhang, Feiran Huang, Xiao Huang, ACL Findings, 2024.

3. Structure Guided Large Language Model for SQL Generation, Qinggang Zhang, Junnan Dong, Hao Chen, Wentao Li, Feiran Huang, Xiao Huang, arXiv:2402.13284, 2024.

Recommender Systems based on LLMs

4. Enhancing Explainable Rating Prediction through Annotated Macro Concept, Huachi Zhou, Shuang Zhou, Hao Chen, Ninghao Liu, Fan Yang, Xiao Huang, ACL, 2024.

5. Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation, Xuansheng Wu, Huachi Zhou, Yucheng Shi, Wenlin Yao, Xiao Huang, Ninghao Liu, TheWebConf, 2024 (ACM version).

Graph-based Retrieval-Augmented Generation

6. KnowGPT: Knowledge Graph based PrompTing for Large Language Models, Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, Xiao Huang, arXiv:2312.06185, 2024.

Knowledge-Graph-based Question Answering

7. Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering, Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, Xiao Huang, ACL, 2024.

8. Cost-efficient Knowledge-based Question Answering with Large Language Models, Junnan Dong, Qinggang Zhang, Chuang Zhou, Hao Chen, Daochen Zha, Xiao Huang, arXiv:2405.17337, 2024.

9. Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs, Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, Zhimeng Jiang, TheWebConf, 2023 (ACM version).

10. Knowledge Graph Embedding Based Question Answering, Xiao Huang, Jingyuan Zhang, Dingcheng Li, Ping Li, WSDM 2019 (ACM version, Slides, Code).

Graph Neural Networks

11. Macro Graph Neural Networks for Online Billion-Scale Recommender Systems, Hao Chen, Yuanchen Bei, Qijie Shen, Yue Xu, Sheng Zhou, Wenbing Huang, Feiran Huang, Senzhang Wang, Xiao Huang, TheWebConf, 2024 (ACM version).

12. Collaborative Graph Neural Networks for Attributed Network Embedding, Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen, Jundong Li, Xia Hu, TKDE, 2023.

13. RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computation, Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha, Xiao Huang, Xia Hu, ICML 2023.

14. Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering, Huachi Zhou, Hao Chen, Junnan Dong, Daochen Zha, Chuang Zhou, Xiao Huang, SIGIR, 2023 (ACM version).

15. Dirichlet Energy Constrained Learning for Deep Graph Neural Networks, Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu, NeurIPS 2021.

16. Towards Deeper Graph Neural Networks with Differentiable Group Normalization, Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu, NeurIPS 2020.

Service

• Organization committee member: ICDE 2025 (PhD Symposium Chair)

• PC member: ICLR 2022-2024, IJCAI 2020-2024, NeurIPS 2021-2024, AAAI 2021-2023, KDD 2019-2023, TheWebConf 2022-2023, ICML 2021-2023, CIKM 2019-2022, WSDM 2021-2023, SDM 2022, ICKG 2020-2021

• Reviewer: TPAMI (2023-), IEEE TKDE (2018-2024), ACM TKDD, ACM Computing Surveys, ACM TOIS, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, IEEE TCSS, ACM TIST, International Journal of Machine Learning and Cybernetics - Springer

• Session Chair: WSDM 2023, IJCAI 2023

• Editorial board member: Computers & Education: X Reality

Awards and Patents

• Best Paper Award Honorable Mention at SIGIR 2023

• Best Poster Presentation Award at PolyU Academy for Interdisciplinary Research Conference 2023

• BEST PAPER at the International Conference On Web-Based Learning (ICWL 2022), Tenerife, Spain

• United States Patent, KNOWLEDGE-GRAPH-EMBEDDING-BASED QUESTION ANSWERING,
  US 2020/0242444 A1

• 2019 INFORMS QSR Best Student Paper Finalist, Seattle, USA

• 2019 INFORMS QSR Best Refereed Paper Finalist, Seattle, USA

• SDM 2017 Doctoral Forum Best Poster Runner-Up Award, Houston, USA

Talks

• [2023/03] Keynote on Machine Learning on Graphs on WSDM 2023 workshop, Singapore

• [2022/08] Talk on Trustworthy Graph Neural Networks on SMP 2022 workshop

• [2019/08] Tutorial on learning from networks on KDD 2019, Alaska

Background

I received Ph.D. from Texas A&M University in 2020, under the supervision of Dr. Xia Hu. I received M.S. from Illinois Institute of Technology in 2015, and B.S. from Shanghai Jiao Tong University in 2012. Before my current position, I worked as a research intern at Microsoft Research and Baidu USA.