Yue Huang (黄跃)

PhD Student of Computer and Science Computer Science and Engineering University of Notre Dame Fitzpatrick Hall of Engineering, Notre Dame, IN 46556 USA Email: yhuang37 (at) nd.edu Google Scholar · Github · LinkedIn · X

About Me

I’m a first-year PhD student in MINE Lab of Computer Science and Engineering (CSE) at the University of Notre Dame starting Fall 2024, supervised by Prof. Xiangliang Zhang. I obtained my bachelor’s degree from Sichuan University in 2024. Previously, I was a visiting student under the guidance of Prof. Lichao Sun. This experience was enhanced by mentorship from Prof. Philip S. Yu. Earlier before, I worked under Prof. Tang Jie and Dr. Xiao Liu at Tsinghua University.

Research Interests

My research is centered on three pivotal questions:

• How can we deepen our understanding of the trustworthiness of foundational generative models? This line of inquiry seeks to develop robust frameworks for evaluating trustworthiness and to identify strategies for enhancing the trustworthiness of these models within specific application domains. This includes: TrustLLM (ICML’24), LLM-as-a-Coauthor (NAACL’24), Attack LLM Judge (ACM CCS’24), FakeGPT (WWW’24), Multilingual Alignment (EMNLP’24), HonestLLM (NeurIPS’24), TrustNLP@NAACL’24, and ObscurePrompt, Bias of LLM Judge (ICLR’25).

• Is there a superior approach to achieving Artificial General Intelligence (AGI)? This research emphasizes data-centric methods to enable scalable model alignment and evolution, ensuring that they adhere to human values and ethical paradigms throughout the development process. This includes: AlignBench (ACL’24), Multilingual Alignment (EMNLP’24), and DataGen (ICLR’25), Interleave Generation (ICLR’25).

• To what extent can current AI technologies effectively benefit downstream applications? This research area critically assesses the practical impact of generative models, with a particular focus on their application and AI4Science, exploring its transformative potential and interdisciplinary contributions in fields such as agentic models, social sciences, and beyond. This includes: MetaTool (ICLR’24), PsychometricBench, and GUI-World (ICLR’25).

News

I will give four invited talks/guest lectures about the ‘‘Socially Responsible and Trustworthy Generative Model’’ @USC (2.24) @UVA (3.24) @KAUST (4.7~4.10) @UIUC (5.9)

Check out latest preprints: Trustworthiness of Generative Foundation Models, Contextual Distraction Vulnerability of LLM, HHH Principles, Preference Leakage of LLM Judge

• 2025.01   Four papers have been accepted by ICLR 2025! I was selected as KAUST Rising Stars in AI Symposium 2025 (24/300+), hosted by the KAUST Center of Excellence for Generative AI in Saudi Arabia from April 7-10, 2025.

• 2024.12   I will join IBM Research as a Research Scientist Intern in 2025 Summer. See you in Cambridge, MA.

• 2024.09   HonestLLM has been accepted by NeurIPS 2024. Congratulations to Chujie! Another paper has been accepted by main conference of EMNLP 2024.

• 2024.08   Attack LLM-as-a-Judge has been accepted by ACM CCS 2024.

• 2024.07   OpenAI’s Researcher Access Program is Awarded.

• 2024.05   TrustLLM has been accepted by ICML 2024. Another paper has been accepted by main conference of ACL 2024.

• 2024.03   One paper has been accepted by NAACL 2024! Congratulations to Qihui, Chujie and Dongping! Another paper has been accepted as a short paper of WWW 2024!

• 2024.01   MetaTool has been accepted by ICLR 2024! I will give an invited talk on TrustLLM Project @ IBM Research.

Selected Publications

Disclaimer: This material is presented to ensure the timely dissemination of scholarly works. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms invoked by each author’s copyright.

*: Equal Contribution

• DataGen: Unified Synthetic Dataset Generation via Large Language Models
  Yue Huang*, Siyuan Wu*, Chujie Gao, Dongping Chen, Qihui Zhang, Yao Wan, Tianyi Zhou, Chaowei Xiao, Jianfeng Gao, et al.
  The Thirteenth International Conference on Learning Representations (ICLR 2025)
  [Toolkit] [Website] [Tutotial Video]

• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
  Jiayi Ye*, Yanbo Wang*, Yue Huang*, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Chao Huang, Pin-Yu Chen, Nitesh V Chawla, Xiangliang Zhang
  The Thirteenth International Conference on Learning Representations (ICLR 2025)
  [Website]

• GUI-World: A GUI-oriented Dataset for Multimodal LLM-based Agents
  Dongping Chen*, Yue Huang*, Siyuan Wu, Jingyu Tang, Huichi Zhou, Qihui Zhang, Zhigang He, et al.
  The Thirteenth International Conference on Learning Representations (ICLR 2025)
  [Website] [Dataset] [Model]

• Interleaved Scene Graph for Interleaved Text-and-Image Generation Assessment
  Dongping Chen, Ruoxi Chen, Shu Pu, Zhaoyi Liu, Yanru Wu, Caixi Chen, Benlin Liu, Yue Huang, Yao Wan, Pan Zhou, Ranjay Krishna
  The Thirteenth International Conference on Learning Representations (ICLR 2025 Spotlight)
  [Website] [Code]

• UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation
  Qihui Zhang, Munan Ning, Zheyuan Liu, Yanbo Wang, Jiayi Ye, Yue Huang, Shuo Yang, Xiao Chen, Yibing Song, Li Yuan
  The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2025 (CVPR 2025)

• TRUSTEVAL: A Dynamic Evaluation Toolkit on Trustworthiness of Generative Foundation Models
  Yanbo Wang*, Jiayi Ye*, Siyuan Wu*, Chujie Gao, Yue Huang, Xiuying Chen, Yue Zhao, Xiangliang Zhang
  2025 Annual Conference of the North American Chapter of the Association for Computational Linguistics – System Demonstration (NAACL 2025 Demo)
  [Code] [Tutotial Video] [Docs.]

• TrustLLM: Trustworthiness in Large Language Models
  Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, et al.
  2024 International Conference on Machine Learning (ICML 2024)
  (Highlighted by United States Department of Homeland Security (DHS) & International AI Safety Report, Invited Talk at IBM Research)
  [Code&Toolkit] [Website] [Dataset] [Docs.]

• MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
  Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, et al.
  The Twelfth International Conference on Learning Representations (ICLR 2024)
  [Code]

• 1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
  Yue Huang*, Chenrui Fan*, Yuan Li, Siyuan Wu, et al.
  The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)

• HonestLLM: Toward an Honest and Helpful Large Language Model
  Chujie Gao*, Siyuan Wu*, Yue Huang*, Dongping Chen*, Qihui Zhang*, et al.
  Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
  [Code]

• Optimization-based Prompt Injection Attack to LLM-as-a-Judge
  Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, Neil Zhenqiang Gong
  The ACM Conference on Computer and Communications Security (ACM CCS 2024)
  [Code]

• LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?
  Qihui Zhang*, Chujie Gao*, Dongping Chen*, Yue Huang, et al.
  2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Findings of NAACL 2024)
  [Code] [Website]

• AlignBench: Benchmarking Chinese Alignment of Large Language Models
  Xiao Liu*, Xuanyu Lei*, Shengyuan Wang, Yue Huang, Zhuoer Feng, et al.
  The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)
  [Code] [Website]

• From Creation to Clarification: ChatGPT’s Journey Through the Fake News Quagmire
  Yue Huang, Kai Shu, Philip S. Yu, Lichao Sun
  2024 ACM Web Conference (WWW 2024)

Talks

• On the Trustworthiness of Generative Foundation Models | Apr. 2025
  @ KAUST Rising Stars in AI Symposium 2025

• Trustworthiness in Large Language Models | Mar. 2025
  @ University of Virginia

• Toward Socially Impactful and Trustworthy Generative Foundation Models | Feb. 2025
  @ University of Southern California [Slides]

• Bias of Large Language Models | Jul. 2024
  @ Technical University of Munich

• Trustworthiness in Large Language Models | Feb. 2024
  @ IBM Research

Honors and Awards

• 2025.01 KAUST AI Rising Star

• 2024.07 OpenAI’s Researcher Access Program

• 2024.01 Microsoft Accelerate Foundation Models Research

Academic Participation

• Journal Reviewer: IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), ACM Transactions on Intelligent Systems and Technology (ACM TIST)

• Conference Reviewer: NeurIPS, ICLR, ICML, ICDM, WWW, ACL Rolling Review, EMNLP Demo Track (2024), NAACL Demo Track (2025), ACL Demo Track (2025)

• Technical Committee Member of 2024 IEEE Computer Society North America Student Challenge

Educations

• 2024.09 - Present, Ph.D, University of Notre Dame
• 2020.09 - 2024.06, BEng., Sichuan University

Internships

• 2023.09 - 2024.01, Research Intern at Tsinghua University KEG

Misc

• I spent 18 years in my hometown, Fujian 🇨🇳, and had 4 wonderful years of university life in Sichuan 🌶️ (I can handle spicy food!).

• I love exchanging ideas with people from different fields 🌍—it helps me see the world more broadly.

• My favorite singers are Eason Chan and Steve Chou;小剛 🎤. Lately, I’ve been listening to Evangeline Wong;王艷薇 🎶.

• My favorite sports are swimming 🏊 and badminton 🏸. I also enjoy capturing scenic moments with my camera 📷.

• I’m deeply grateful to those who’ve helped me along the way 🙏—thank you for helping me go further!