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Hongfu Liu (刘洪甫)

Contact: liu (dot) hongfu [at] u (dot) nus (dot) edu

Hi! I am a third-year Ph.D. student in the Sound and Music Computing Lab at National University of Singapore. I am fortunate to be advised by Prof. Ye Wang. Previously, I obtained my Bachelor’s degree in Computer Science at Zhejiang University.

Currently, I focus more on improving large language (foundation) models by: (1) designing data-efficient approaches; (2) understanding the safety and robustness; (3) evaluating large language model agents.

I am always open to collaborations and feel free to reach out if you share similar interests!

News

May, 2024 Two papers (1 Main and 1 Finding) got accepted to ACL 2024! Congrats to all co-authors!
Oct, 2023 One paper got accepted to the Findings of EMNLP 2023!
May, 2023 One paper got accepted to Interspeech 2023!
Nov, 2022 One paper got accepted to Advances in Neural Information Processing Systems 2022!
May, 2022 I passed my Ph.D. Qualifying Examination (PQE) and became a Ph.D. candidate!
May, 2021 One paper got accepted to International Conference on Machine Learning 2021!

Selected Publications

  1. ACL
    Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations
    Yisong Miao, Hongfu Liu, Wenqiang Lei, Nancy F. Chen, and Min-Yen Kan
    In Association for Computational Linguistics 2024
  2. ACL Findings
    Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset
    Hengguan Huang, Songtao Wang, Hongfu Liu, Hao Wang, and Ye Wang
    In Findings of Association for Computational Linguistics 2024
  3. arXiv
    Advancing Test-Time Adaptation for Acoustic Foundation Models in Open-World Shifts
    Hongfu Liu, Hengguan Huang, and Ye Wang
    In arXiv preprint 2023
  4. EMNLP Findings
    Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning
    Hongfu Liu, and Ye Wang
    In Findings of the Empirical Methods in Natural Language Processing 2023
  5. NeurIPS
    Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation
    Hengguan Huang, Xiangming Gu, Hao Wang, Chang Xiao, Hongfu Liu, and Ye Wang
    In Advances in Neural Information Processing Systems 2022
  6. ICML
    STRODE: Stochastic Boundary Ordinary Differential Equation
    Hengguan Huang, Hongfu Liu, Hao Wang, Chang Xiao, and Ye Wang
    In International Conference on Machine Learning 2021

Academic Reviewers

  • ICLR(2022&2024), ICCV(2023), IJCAI(2024), ECCV(2024)


Teaching

  • CS4347: Sound and Music Computing (2022 Spring / 2022 Fall)

  • CS5242: Neural Networks and Deep Learning (2021 Fall / 2023 Spring)