Jian Guan

Jian Guan 关 健

Empowering the future driven by curiosity.

Beijing, China
Researcher at Ant Group

I currently serve as a researcher at Ant Group (Jul. 2024~), working with Wei Wu. I obtain my bachelor (2015~2019) and Ph.D. (2019~2024) degrees from Tsinghua University, advised by Minlie Huang. I have also interned at various institutions, including the Allen Institute for Artificial Intelligence in Seattle with the supervision of Hao Peng and Jesse Dodge (Jan.~Jun., 2023) and the University of Virginia with the supervision of Hongning Wang(Jul.~Sep., 2018).

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Seeking Self-Motivated Interns

We are currently seeking self-motivated interns to join our team at the Ant Group. If you are interested in working on cutting-edge research in the above research fields, we encourage you to contact us. Our institute offers a dynamic and collaborative environment, with opportunities to work alongside experienced researchers and gain hands-on experience in real-world applications. DO NOT miss this chance to grow your skills and make an impact in the field! Contact us today to learn more about available positions.

🏅 Awards

2024

CIPS Doctoral Dissertation Incentive Program

中国中文信息学会博士学位论文激励计划 (Top 10 in China)

2024

Tsinghua University Excellent Doctoral Dissertation

清华大学优秀博士论文

2022

Microsoft Research Asia Fellowship Nomination Award

微软学者提名奖(Top 33 in Asia)

2022

National Scholarship for Doctoral Students

国家奖学金

2019

Excellent Graduate in Beijing

北京市优秀毕业生

2019

Outstanding Graduate in Tsinghua University

清华大学优良毕业生

Research & Publications

* indicates equal contribution; † indicates corresponding author(s)

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All Publications

All publications sorted by publication year (newest first)

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Advanced Reasoning

Advancing reasoning capabilities in large (vision-)language models for complex problem solving

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Personalized Alignment

Aligning AI systems with individual human preferences and values

Survey
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Natural Language Generation

Natural language generation for open-ended tasks and comprehensive evaluation methodologies

Must-read paper list

Efficient Foundation Models

Developing efficient architectures and training methods for large-scale foundation models