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I am a PhD student at the School of Computing (SoC), National University of Singapore (NUS), majoring in Computer Science. I am fortunate to be supervised by Prof. Byran Hooi. Previously, I worked as a research intern at:
Email  /  Github  /  Google Scholar  /  Twitter |
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The big picture of my research interests lies in democratizing data intelligence to empower people and organizations to derive insights, learn and share knowledge, and build intelligence to turn data into action. Regardless of the various forms of data, understanding, generation, and interaction are the three common themes. Data understanding aims to achieve semantic understanding of various types of data; Data generation aims for automatic content generation based on users' needs; and Data interaction aims to create unparalleled user experiences working with data like recommendation or information retrieval. Specifically, I am interested in the following topics: Development of Large Language Model , Semi-structured Data Modeling & Reasoning , Causal Inference .
A few questions that drive my recent research are:
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Yuan Sui, Bryan Hooi Preprint, 2024 arXiv / code This paper presents OKGQA, a new benchmark for evaluating Knowledge Graph-enhanced LLMs in open-ended question answering, focusing on reducing hallucinations and improving reasoning. It also introduces OKGQA-P to test model performance with perturbed KGs. The work aims to (1) explore whether KGs can make LLMs more trustworthy in an open-ended setting, and (2) conduct a comparative analysis to shed light on methods and future directions for leveraging KGs to reduce LLMs' hallucination. |
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Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi Preprint, 2024 arXiv / code This paper introduces FiDeLiS, a retrieval-exploration interactive method that integrates knowledge graphs (KG) with large language models (LLMs) to enhance reasoning accuracy and reduce hallucinations. By utilizing the Path-RAG module to recall relevant KG knowledge and leveraging LLMs’ deductive reasoning for guiding the reasoning process, FiDeLiS achieves more reliable and efficient question answering performance. |
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Yufei He, Yuan Sui, Xiaoxin He, Bryan Hooi 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25), 2024 arXiv / code This paper introduces UniGraph, a framework for generalizing graph learning across diverse domains using Text-Attributed Graphs (TAGs). It proposes a pre-training algorithm based on masked graph modeling and graph instruction tuning to enable zero-shot and few-shot learning on unseen domains. Experiments on various tasks demonstrate that UniGraph outperforms state-of-the-art methods in cross-domain graph learning. |
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Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, Dongmei Zhang Conference on Empirical Methods in Natural Language Processing (EMNLP'24), 2023 arXiv / poster / slide TAP4LLM presents a framework for effective table reasoning by decomposing large tables, augmenting them with semantic and statistical metadata, and intelligently packing the information for LLM processing. This approach addresses performance issues with huge tables and complex questions by ensuring essential information is well-organized and enriched. |
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Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han and Dongmei Zhang Conference on Web Search and Data Mining (WSDM'24), Long Paper, 2023 arXiv / code / blog / poster / slide This study introduces a benchmark to evaluate the structural understanding capabilities of LLMs on table data through seven distinct foundation tasks. Evaluations on GPT-3.5 and GPT-4 reveal that input formatting and structural prompting significantly impact performance. The proposed self-augmentation structural prompting methods improve LLM performance on multiple tabular tasks, providing a foundation for future research in table comprehension by LLMs. |
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Weicheng Ma, Junhwi Kim, Yuan Sui, Chunyuan Deng, Lili Wang and Soroush Vosoughi Preprint, 2023 arXiv / code The paper investigates why cross-lingual fine-tuning underperforms compared to multi-lingual fine-tuning, It proposes target-language text-domain adaptation and feature augmentation to enhance cross-lingual models, effectively narrowing the performance gap. These methods offer practical strategies for improving cross-lingual fine-tuning, especially for low-resource languages. |
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Wei Yan, Yu Shi, Zengyan Ji, Yuan Sui, Zhenzhen Tian, Wanjing Wang, Qiushi Cao Engineering Applications of Artificial Intelligence , 2023 (IF=8) This research proposes a virtual knowledge graph-based approach for the digital modeling and intelligent predictive maintenance of hydraulic systems in manufacturing. By integrating heterogeneous data from sensing networks and structuring domain knowledge, the approach facilitates effective data access, integration, and predictive analytics in life-cycle of predictive maintenance. |
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Yuan Sui, Shanshan Feng, Huaxiang Zhang, Jian Cao, Liang Hu, Nengjun Zhu Knowledge-Based Systems (KBS), 2022 (IF=8.139) The paper presents CF-KGQA, a causal filter model that improves knowledge graph-based question answering by addressing spurious relations through causal interference in the relation representation space. By employing a causal pairwise aggregator and a disentangled latent factor aggregator, CF-KGQA reduces erroneous relation representations and enhances robustness on edge cases. |
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Yuan Sui, Fanyang Bu, Yingting Hu, Wei Yan, Liang Zhang International Joint Conference on Neural Networks (IJCNN'22), Long Paper, 2022 (oral) Trigger-GNN introduces a graph neural network that leverages entity triggers and complementary annotations to improve nested named entity recognition. By encoding entity triggers and utilizing an efficient message-passing architecture, the model gains the capability to effectively identifies and categorizes complex hierarchical entity structures. |
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Data,
Knowledge, and Intelligence Group
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Microsoft Research Asia (MSRA)
Research Intern | Aug. 2022 to June. 2023 Advisor: Dr. Mengyu Zhou |
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Minds,
Machines and Society Lab, Dartmouth College
Research Intern | June. 2022 to Feb. 2023 Advisor: Prof. Soroush Vosoughi |
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Visual Information
Processing and Learning (VIPL)Lab, ICT,
Chinese Academy of Sciences
Research Intern | Feb. 2022 to June. 2022 Advisor: Prof. Shuhui Wang |
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Thanks Jon Barron
& Tairan He
for this amazing template.
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