Jinghan Jia

Selected publications

1. ICML’25

   Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond

   Chongyu Fan*, Jinghan Jia*, Yihua Zhang, and 3 more authors

   In The Forty-Second International Conference on Machine Learning 2025

   Abs Paper Code

   "The LLM unlearning technique has recently been introduced to comply with data regulations and address the safety and ethical concerns of LLMs by removing the undesired data-model influence. However, state-of-the-art unlearning methods face a critical vulnerability: they are susceptible to "relearning" the removed information from a small number of forget data points, known as relearning attacks. In this paper, we systematically investigate how to make unlearned models robust against such attacks. For the first time, we establish a connection between robust unlearning and sharpness-aware minimization (SAM) through a unified robust optimization framework, in an analogy to adversarial training designed to defend against adversarial attacks. Our analysis for SAM reveals that smoothness optimization plays a pivotal role in mitigating relearning attacks. Thus, we further explore diverse smoothing strategies to enhance unlearning robustness. Extensive experiments on benchmark datasets, including WMDP and MUSE, demonstrate that SAM and other smoothness optimization approaches consistently improve the resistance of LLM unlearning to relearning attacks. Notably, smoothness-enhanced unlearning also helps defend against (input-level) jailbreaking attacks, broadening our proposal’s impact in robustifying LLM unlearning."

   @inproceedings{fan2025towards,
     title = {Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond},
     author = {Fan*, Chongyu and Jia*, Jinghan and Zhang, Yihua and Ramakrishna, Anil and Hong, Mingyi and Liu, Sijia},
     year = {2025},
     booktitle = {The Forty-Second International Conference on Machine Learning},
   }

2. NeurIPS’24

   WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models

   Jinghan Jia, Jiancheng Liu, Yihua Zhang, and 3 more authors

   In The Thirty-eighth Annual Conference on Neural Information Processing Systems 2024

   Abs Paper Code

   "The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. LLM unlearning is designed to reduce the impact of undesirable data influences and associated model capabilities without diminishing the utility of the model if unrelated to the information being forgotten. Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined. In this paper, we systematically explore how model weights interact with unlearning processes in LLMs and we design the weight attribution-guided LLM unlearning method, WAGLE, which unveils the interconnections between ’influence’ of weights and ’influence’ of data to forget and retain in LLM generation. By strategically guiding the LLM unlearning across different types of unlearning methods and tasks, WAGLE can erase the undesired content, while maintaining the performance of the original tasks. We refer to the weight attribution-guided LLM unlearning method as WAGLE, which unveils the interconnections between ’influence’ of weights and ’influence’ of data to forget and retain in LLM generation. Our extensive experiments show that WAGLE boosts unlearning performance across a range of LLM unlearning methods such as gradient difference and (negative) preference optimization, applications such as fictitious unlearning (TOFU benchmark), malicious use prevention (WMDP benchmark), and copyrighted information removal, and models including Zephyr-7b-beta and Llama2-7b. To the best of our knowledge, our work offers the first principled method for attributing and pinpointing the influential weights in enhancing LLM unlearning. It stands in contrast to previous methods that lack weight attribution and simpler weight attribution techniques."

   @inproceedings{jia2024wagle,
     title = {WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models},
     author = {Jia, Jinghan and Liu, Jiancheng and Zhang, Yihua and Ram, Parikshit and Baracaldo, Nathalie and Liu, Sijia},
     year = {2024},
     booktitle = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
   }

3. EMNLP’24

   Soul: Unlocking the power of second-order optimization for llm unlearning

   Jinghan Jia, Yihua Zhang, Yimeng Zhang, and 5 more authors

   In The 2024 Conference on Empirical Methods in Natural Language Processing 2024

   Paper Code

   @inproceedings{jia2024soul,
     title = {Soul: Unlocking the power of second-order optimization for llm unlearning},
     author = {Jia, Jinghan and Zhang, Yihua and Zhang, Yimeng and Liu, Jiancheng and Runwal, Bharat and Diffenderfer, James and Kailkhura, Bhavya and Liu, Sijia},
     journal = {arXiv preprint arXiv:2404.18239},
     year = {2024},
     publisher = {EMNLP 2024},
     booktitle = {The 2024 Conference on Empirical Methods in Natural Language Processing},
   }

4. ECCV’24

   To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now

   Yimeng Zhang*, Jinghan Jia*, Xin Chen, and 5 more authors

   In European Conference on Computer Vision 2024

   Paper Code

   @inproceedings{zhang2023generate,
     title = {To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now},
     author = {Zhang*, Yimeng and Jia*, Jinghan and Chen, Xin and Chen, Aochuan and Zhang, Yihua and Liu, Jiancheng and Ding, Ke and Liu, Sijia},
     publisher = {ECCV 2024},
     booktitle = {European Conference on Computer Vision},
     year = {2024}
   }

5. NAACL’24

   Leveraging LLMs for dialogue quality measurement

   Jinghan Jia, Abi Komma, Timothy Leffel, and 5 more authors

   In 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics 2024

   Paper
6. NeurIPS’23

   Model Sparsity Can Simplify Machine Unlearning

   Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, and 5 more authors

   In Thirty-seventh Conference on Neural Information Processing Systems 2023

   Paper Code

   @inproceedings{jia2023model,
     title = {Model Sparsity Can Simplify Machine Unlearning},
     author = {Jia*, Jinghan and Liu*, Jiancheng and Ram, Parikshit and Yao, Yuguang and Liu, Gaowen and Liu, Yang and Sharma, Pranay and Liu, Sijia},
     year = {2023},
     publisher = {NeurIPS 2023},
     booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
   }

7. SANER’23

   CLAWSAT: Towards Both Robust and Accurate Code Models

   Jinghan Jia*, Shashank Srikant*, Tamara Mitrovska, and 4 more authors

   In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) 2023

   Paper Code Poster

     doi = {10.48550/ARXIV.2211.11711},
     author = {Jia*, Jinghan and Srikant*, Shashank and Mitrovska, Tamara and Gan, Chuang and Chang, Shiyu and Liu, Sijia and O'Reilly, Una-May},
     keywords = {Machine Learning (cs.LG), Programming Languages (cs.PL), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences},
     booktitle = {2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)},
     title = {CLAWSAT: Towards Both Robust and Accurate Code Models},
     year = {2023},
     copyright = {Creative Commons Attribution 4.0 International}
   }