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Hi there, thanks for visiting my website! I am a third-year undergraduate student at Xi'an Jiaotong University, majoring in Computer Science. I am interested in natural language reasoning and how to use language to interact with the physical world and humans, advised by Prof. Minnan Luo. I have interned at UW NLP with Ph.D. student Shangbin Feng and Prof. Yulia Tsvetkov. I am now working with Ph.D. student Ruiqi Zhong and Prof. Jacob Steinhardt at UC Berkeley. My previous research includes natural language reasoning, spoiler detection, and AI agent safety. I'm a member of LUD lab, the premiere undergraduate research group @ XJTU. Email / CV / Google Scholar / Twitter / Github |
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I am interested in natural language reasoning and how to use language to interact with the physical world and humans. |
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Heng Wang*, Shangbin Feng*, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, Yulia Tsvetkov Proceedings of NeurIPS, 2023 (spotlight; 3.4% acceptance rate) code / poster Are language models graph reasoners? We propose the NLGraph benchmark, a test bed for graph-based reasoning designed for language models in natural language. We find that LLMs are preliminary graph thinkers while the most advanced graph reasoning tasks remain an open research question. |
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Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Qinghua Zheng, Minnan Luo Proceedings of EMNLP, 2023 code We curate a large-scale network-based spoiler detection dataset (LCS), a movie knowledge base (UKM), and propose MVSD, a Multi-View Spoiler Detection framework that takes into account external knowledge and user interaction networks. |
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Yike Wang*, Shangbin Feng*, Heng Wang, Weijia Shi, Vidhisha Balachandran , Tianxing He, Yulia Tsvetkov arXiv preprint, 2023. code We introduce KNOWLEDGE CONFLICT, an evaluation framework for simulating contextual knowledge conflicts and quantitatively evaluating LLMs' abilities to handle knowledge conflicts. |
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Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng, Qinghua Zheng, Minnan Luo Proceedings of SIGIR 2023. code We propose community-aware mixture-of-experts to address two challenges in detecting advanced Twitter bots: manipulated features and diverse communities. |
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Shangbin Feng*, Zhaoxuan Tan*, Herun Wan*, Ningnan Wang*, Zilong Chen*, Binchi Zhang*, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, Yuhan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, Minnan Luo Proceedings of NeurIPS, Datasets and Benchmarks Track, 2022. website / GitHub / bibtex / poster We present TwiBot-22, the largest graph-based Twitter bot detection benchmark to date, which provides diversified entities and relations in Twittersphere and has considerably better annotation quality. |
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Xi'an Jiaotong University
2021.09 - 2025.07 (Expected) B.E. in Computer Science GPA: 94.96 / 100.0 (4.17 / 4.3) Advisor: Prof. Minnan Luo |
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University of California, Berkeley
2023.08 - 2023.12 BGA Student (Visiting Student) GPA: 4.00 / 4.00 |
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Luo lab Undergraduate Division (LUD) @ XJTU
Member 2021.08 - present Conducted research on various topics including social network analysis, and graph neural networks pre-training. Advisor: Prof. Minnan Luo |
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Template courtesy: Jon Barron. |