Papers by Hong Ting Tsang
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)
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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song
| Challenge: | Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures. |
| Approach: | They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). |
| Outcome: | The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs. |
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents. |
| Approach: | They introduce a foundational three-level taxonomy to delineate their escalating autonomy and evolving responsibilities within the research lifecycle. |
| Outcome: | The proposed frameworks provide a conceptual architecture and strategic foresight to navigate and shape the future of AI-driven scientific discovery. |
ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance (2026.acl-long)
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Haoran Li, Yulin Chen, Huihao Jing, Wenbin Hu, Tsz Ho Li, Chanhou Lou, Hong Ting Tsang, Sirui Han, Yangqiu Song
| Challenge: | Existing approaches to contextualize safety and privacy assessments assume the availability of complete and clear context, whereas real-world contexts tend to be ambiguous and incomplete. |
| Approach: | They propose a semi-rule-based framework that leverages large language models to ground the input context in the legal domain and explicitly identify both known and unknown factors for legal compliance. |
| Outcome: | The proposed framework can significantly improve existing baselines without training and can identify the ambiguous and missing factors. |