Papers by Feng Yichao

2 papers
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation (2025.naacl-long)

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Challenge: Existing approaches to multimodal entity linking use contrastive learning to align input sentences and entities, but are limited by their random negative sampling.
Approach: They propose a method to match negative samples with similar attributes using JD-CCL . they also propose 'contextual visual-aid controllable patch transform' experimental results demonstrate the strong effectiveness of their method .
Outcome: The proposed method is able to match negative samples with similar attributes on a multimodal knowledge graph.
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation (2025.findings-acl)

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Challenge: Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models and downstream tasks, but is vulnerable to malicious attacks.
Approach: They propose a weak-to-strong unlearning algorithm based on feature alignment knowledge distillation to defend against backdoor attacks . they first train a small-scale language model through full-parameter fine-tuning to serve as the clean teacher model and then guide the large-scale poisoned student model in unlearning the backdoor.
Outcome: The proposed method can unlearn backdoor features without compromising model performance.

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