Yuyang Bai  (白雨洋)

Hi there, thanks for visiting my website! I am a junior at Xi'an Jiaotong University, majoring in Artificial Intelligence. I'm looking for Ph.D. positions starting in 25Fall.

I am interested in reasoning in natural language processing and how to evaluate and improve the knowledge ability of large language models to better address real-world problems. I'm a member of LUD lab, the premiere undergraduate research group @ XJTU, advised by Prof. Minnan Luo. I have interned at UW NLP with Ph.D. student Shangbin Feng and Prof. Yulia Tsvetkov. I am now visiting at the University of Notre dame, working with Ph.D. student Qingkai Zeng, Zhaoxuan Tan and Prof. Meng Jiang. My previous research includes social network analysis and knowledge graphs.

Email:  yuyangbai2002 [at] gmail [dot] com  /  1206944633 [at] stu [dot] xjtu [dot] edu [dot] cn

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Research Interest

My research interests include:

  • Interpreting and enhancing the knowledge & reasoning ability of Large Language Models
  • NLP & social network analysis for fairness and common good

πŸ”₯What's New
Publications (* indicates equal contribution)
3DSP KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
Yuyang Bai*, Shangbin Feng*, Vidhisha Balachandran, Zhaoxuan Tan, Shiqi Lou, Tianxing He, Yulia Tsvetkov
Proceedings of TheWebConf (WWW), 2024 (oral).

We propose KGQuiz, a knowledge-intensive benchmark to evaluate the generalizability of LLM knowledge abilities across knowledge domains and progressively complex task formats.

3DSP Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Qingkai Zeng*, Yuyang Bai*, Zhaoxuan Tan, Shangbin Feng, Zhenwen Liang, Zhihan Zhang, Meng Jiang
arxiv preprint 2024.

In this work, we introduce Chain-of-Layer (CoL), a novel framework for taxonomy induction. By leveraging the hierarchical format instruction (HF) and incorporating an Ensemble-based Ranking Filter, CoL breaks down the task into selecting relevant candidates and gradually building the taxonomy from top to bottom and significantly reduces hallucination and improves structural accuracy.

3DSP Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov
Proceedings of ICLR, 2024 (oral).

We propose Knowledge Card, a community-driven initiative to empower black-box LLMs with modular and collaborative knowledge. By incorporating the outputs of independently trained, small, and specialized LMs, we make LLMs better knowledge models by empowering them with temporal knowledge update, multi-domain knowledge synthesis, and continued improvement through collective efforts.

3DSP FACTKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge
Shangbin Feng, Vidhisha Balachandran, Yuyang Bai, Yulia Tsvetkov
Proceedings of EMNLP, 2023.
code / bibtex

We propose a simple, easy-to-use, shenanigan-free summarization factuality evaluation model by augmenting language models with factual knowledge from knowledge bases.

3DSP Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Qinghua Zheng, Minnan Luo
Proceedings of EMNLP, 2023.
code / bibtex

We propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms.

3DSP TwiBot-22: Towards Graph-Based Twitter Bot Detection
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 the 2022 NeurIPS Datasets and Benchmarks Track, 2022.
website / code / bibtex / poster

We present Twibot-22, the largest graph-based Twibot bot detection benchmark to date, which provides diversified entities and relations in Twittersphere and has considerably better annotation quality.

Xi'an Jiaotong University
2021.09 - 2025.07 (Expected)

B.E. in Artificial Intelligence
GPA: 91.9 / 100.0
Advisor: Prof. Minnan Luo
University of Notre Dame
2023.08 - 2023.12

Non-degree Undergraduate (Exchange Student)
GPA: 3.92 / 4.0
Advisor: Prof. Meng Jiang
Academic Experiences
Luo lab Undergraduate Division (LUD) @ XJTU

Member         2021.08 - present
Conducted research on various topics including social network analysis, knowledge graphs, and graph neural networks.
Advisor: Prof. Minnan Luo

Template courtesy: Jon Barron.