Papers by Hankun Kang
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models (2026.findings-acl)
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| Challenge: | Existing defense methods rely on fine-tuning or inefficient post-hoc interventions, limiting their ability to address novel attacks. |
| Approach: | They propose a decoding-level defense mechanism that employs a lightweight discriminator to iteratively steer the decoding process toward safety. |
| Outcome: | The proposed method improves safety performance by up to 33.40% without fine-tuning on multiple MLLMs. |
Implanting LLM’s Knowledge via Reading Comprehension Tree for Toxicity Detection (2024.findings-acl)
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| Challenge: | Existing methods for toxic content detection are small language model (SLM) based and large language model(LLM) -based. |
| Approach: | They propose to implant LLM's knowledge into SLM based methods to stick to both types of models' strengths by constructing a reading comprehension tree to transfer knowledge between two models. |
| Outcome: | The proposed method can stick to both types of models' strengths . it is compared with existing methods on real-world and machine-generated datasets. |