Papers by Yuqi Ding
AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders (2025.findings-emnlp)
Copied to clipboard
| Challenge: | AMIA is a lightweight, inference-only defense for Large Vision–Language Models . it automatically masks text-irrelevant image patches and conducts joint Intention Analysis . |
| Approach: | AMIA is a lightweight, inference-only defense for large vision–language models . it automatically masks a small set of text-irrelevant image patches to disrupt adversarial perturbations . |
| Outcome: | AMIA improves defense success rates across diverse LVLMs and jailbreak benchmarks . it preserves general utility with only 2% accuracy drop, incurs only modest inference overhead . |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
Copied to clipboard
Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
Intention Analysis Makes LLMs A Good Jailbreak Defender (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to align large language models with human values overlook the intrinsic nature of jailbreaks, which limits their effectiveness in complex scenarios. |
| Approach: | They propose a simple yet highly effective defense strategy, i.e., Intention Analysis (IA). They show that IA suppresses LLM’s tendency to follow jailbreak prompts, thereby enhancing safety. |
| Outcome: | The proposed strategy reduces harmfulness of LLMs and outperforms GPT-3.5 in attack success rate. |
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)
Copied to clipboard
Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao
| Challenge: | Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding. |
| Approach: | They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss . |
| Outcome: | The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss. |