Haoran Li 李浩然Ph.D.
Computer Science
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I am currently a fourth-year Ph.D. student majoring in Computer Science at the Hong Kong University of Science and Technology advised by Prof. Yangqiu Song. I obtained my B.S. degree, majoring in Computer Science and Math-CS track, from the Hong Kong University of Science and Technology in 2020.
I was an intern student in the Toutiao AI Lab, Bytedance for NLP research during Summer of 2022.My research interest is mainly about privacy studies in NLP that include:
Multi-step Jailbreaking Privacy Attacks on ChatGPT
Haoran Li*, Dadi Guo*, Wei Fan, Mingshi Xu, Jie Huang, Fanpu Meng, Yangqiu Song Findings of EMNLP 2023 [ Code ] [ Paper ] In this paper, we study the privacy threats from OpenAI's model APIs and New Bing enhanced by ChatGPT and show that application-integrated LLMs may cause more severe privacy threats ever than before. |
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Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence
Haoran Li, Mingshi Xu, Yangqiu Song Findings of ACL 2023 [ Code ] [ Paper ] In this work, we further investigate the information leakage issue and propose a generative embedding inversion attack (GEIA) that aims to reconstruct input sequences based only on their sentence embeddings. |
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You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas
Haoran Li, Yangqiu Song, Lixin Fan NAACL 2022 (Oral) [ Code ] [ Paper ] We investigate the privacy leakage of the hidden states of chatbots trained by language modeling which has not been well studied yet. We show that speakers' personas can be inferred through a simple neural network with high accuracy. To this end, we propose effective defense objectives to protect persona leakage from hidden states. |
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FedAssistant: Dialog Agents with Two-side Modeling
Haoran Li*, Ying Su*, Qi Hu, Jiaxin Bai, Yilun Jin, Yangqiu Song FL-IJCAI'22 [ Code ] ] [ Paper ] We propose a framework named FedAssistant to training neural dialog systems in a federated learning setting. Our framework can be trained on multiple data owners with no raw data leakage during the process of training and inference. (Code and paper will appear later.) |
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Differentially Private Federated Knowledge Graphs Embedding
Hao Peng*, Haoran Li*, Yangqiu Song, Vincent Zheng, Jianxin Li CIKM 2021 (Oral) [ Code ] [ Paper ] We propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and peer-to-peer manner while being privacy-preserving. |
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Self-supervised Dance Video Synthesis Conditioned on Music
Xuanchi Ren, Haoran Li, Zijian Huang, Qifeng Chen ACM International Conference on Multimedia (ACM MM), 2020 (Oral) [ Code ] [ Paper ] Undergraduate Final Year Project. |
Reviewer at ARR Rolling Review |
Reviewer at KDD 2023 |