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1 | 1 | const publications = [
|
| 2 | + { |
| 3 | + "title": "AutoScale: Scale-Aware Data Mixing for Pre-Training LLMs", |
| 4 | + "authors": "Feiyang Kang, Yifan Sun, Bingbing Wen, Si Chen, Dawn Song, Rafid Mahmood, Ruoxi Jia", |
| 5 | + "conference": "Conference on Language Modeling (COLM), 2025", |
| 6 | + "arxiv": "https://arxiv.org/abs/2407.20177", |
| 7 | + "highlights": [] |
| 8 | + }, |
| 9 | + { |
| 10 | + "title": "Data-Centric Human Preference with Rationales for Direct Preference Alignment", |
| 11 | + "authors": "Hoang Anh Just, Ming Jin, Anit Kumar Sahu, Huy Phan, Ruoxi Jia", |
| 12 | + "conference": "Conference on Language Modeling (COLM), 2025", |
| 13 | + "arxiv": "https://arxiv.org/abs/2407.14477", |
| 14 | + "highlights": [] |
| 15 | + }, |
| 16 | + { |
| 17 | + "title": "LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models", |
| 18 | + "authors": "Minqian Liu, Zhiyang Xu, Xinyi Zhang, Heajun An, Sarvech Qadir, Qi Zhang, Pamela J. Wisniewski, Jin-Hee Cho, Sang Won Lee, Ruoxi Jia, Lifu Huang", |
| 19 | + "conference": "Conference on Language Modeling (COLM), 2025", |
| 20 | + "arxiv": "https://arxiv.org/abs/2504.10430", |
| 21 | + "highlights": [] |
| 22 | + }, |
| 23 | + { |
| 24 | + "title": "NaviDet: Efficient Input-level Backdoor Detection on Text-to-Image Synthesis via Neuron Activation Variation", |
| 25 | + "authors": "Shengfang Zhai, Jiajun Li, Yue Liu, Huanran Chen, Zhihua Tian, Wenjie Qu, Qingni Shen, Ruoxi Jia, Yinpeng Dong, Jiaheng Zhang", |
| 26 | + "conference": "International Conference on Computer Vision (ICCV), 2025", |
| 27 | + "arxiv": "https://arxiv.org/abs/2503.06453", |
| 28 | + "highlights": [] |
| 29 | + }, |
| 30 | + { |
| 31 | + "title": "Model Residuals as Shields: A Two-Level Formulation to Defend Smart Grids From Poisoning Attacks", |
| 32 | + "authors": "Tung-Wei Lin, Padmaksha Roy, Yi Zeng, Ming Jin, Ruoxi Jia, Chen-Ching Liu, Alberto Sangiovanni-Vincentelli", |
| 33 | + "conference": "IEEE Internet of Things Journal, 2025", |
| 34 | + "arxiv": "https://waynelin567.github.io/files/res.pdf", |
| 35 | + "highlights": [] |
| 36 | + }, |
2 | 37 | {
|
3 | 38 | "title": "Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning",
|
4 | 39 | "authors": "Mahavir Dabas, Si Chen, Charles Fleming, Ming Jin, Ruoxi Jia",
|
5 | 40 | "conference": "International Conference on Machine Learning (ICML), 2025",
|
6 |
| - "arxiv": "", |
| 41 | + "arxiv": "https://arxiv.org/abs/2507.04250", |
7 | 42 | "highlights": []
|
8 | 43 | },
|
9 | 44 | {
|
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