Yue Huang (黄跃)
About MeI’m a first-year PhD student in MINE Lab of Computer Science and Engineering (CSE) at the University of Notre Dame began from Fall 2024, supervised by Prof. Xiangliang Zhang. I’m also a graduate student in Foundation Models and Applications Lab (FMAL) at Lucy Family Institute for Data & Society. I obtained my bachelor’s degree from Sichuan University in 2024. I am working with Prasanna Sattigeri this summer at MIT-IBM Watson AI Lab. 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.
I welcome the opportunity to connect with colleagues in my field as well as those from interdisciplinary areas, as I believe collaboration is immensely valuable. Research InterestsMy research is centered on three pivotal questions:
Trustworthy, Aligned, and Democratically Governed Generative Foundation 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: ICML'24, NAACL'24, ACM CCS'24, WWW'24, EMNLP'24, NeurIPS'24, and ICLR'25d.
Data-Driven Scalable Alignment for General-Purpose AI Systems 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: ACL'24, EMNLP'24, ICLR'25a, and ICLR'25b.
Scientific AI and Societal AI 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: ICLR'24, Preprint, and ICLR'25c.
News
Check out latest preprints: AdaReasoner, Trustworthiness of Generative Foundation Models
May. 2025 Two papers are accepted by ACL 2025 (1 Main + 1 Findings).
Jan. 2025 Four papers have been accepted by ICLR 2025! I was selected as KAUST Rising Stars in AI Symposium 2025 (24/300+).
Dec. 2024 I will join IBM Research as a Research Scientist Intern in 2025 Summer. See you in Cambridge, MA.
Sep. 2024 HonestLLM has been accepted by NeurIPS 2024. Congratulations to Chujie! Another paper has been accepted by main conference of EMNLP 2024.
Aug. 2024 Attack LLM-as-a-Judge has been accepted by ACM CCS 2024.
Jul. 2024 OpenAI's Researcher Access Program is Awarded.
May. 2024 TrustLLM has been accepted by ICML 2024. Another paper has been accepted by main conference of ACL 2024.
Mar. 2024 One paper has been accepted by NAACL 2024. Another paper has been accepted as a short paper of WWW 2024.
Jan. 2024 MetaTool has been accepted by ICLR 2024!
Selected PublicationsDisclaimer: 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) 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) 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) 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) Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models Zixiang Xu*, Yanbo Wang*, Yue Huang*, Xiuying Chen, Jieyu Zhao, Meng Jiang, Xiangliang Zhang The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis Yicheng Lang*, Kehan Guo*, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang Findings of The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025 Findings) 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) 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) 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) 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) 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) 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) 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) 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
May. 2025 Toward Socially Impactful and Trustworthy Generative Foundation Models @ University of Illinois Urbana-Champaign [Slides]
Apr. 2025 On the Trustworthiness of Generative Foundation Models @ KAUST Rising Stars in AI Symposium 2025
Mar. 2025 Trustworthiness in Large Language Models @ University of Virginia
Feb. 2025 Toward Socially Impactful and Trustworthy Generative Foundation Models @ University of Southern California [Slides]
Jul. 2024 Bias of Large Language Models @ Technical University of Munich
Feb. 2024 Trustworthiness in Large Language Models @ IBM Research
Honors and Awards
Jan. 2025 KAUST AI Rising Star
Jul. 2024 OpenAI's Researcher Access Program
Jan. 2024 Microsoft Accelerate Foundation Models Research
Academic Participation
Educations
Sep. 2024
Present Ph.D in Computer Science and Engineering
Sep. 2020
Jun. 2024 BEng. in Cybersecurity
Internships
May. 2025
present Research Intern
Sep. 2023
Jan. 2024 Research Intern
Misc
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