Papers by Aofei Chang
CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
| Approach: | They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm . |
| Outcome: | The proposed approach improves on six public ICD coding datasets compared to state-of-the-art models. |
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning (2025.acl-long)
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| Challenge: | Existing methods rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. |
| Approach: | They propose a framework for automatic attention alignment tuning that leverages weak labels from SAM and selectively modifies visually-critical attention heads to improve alignment while minimizing interference. |
| Outcome: | The proposed framework outperforms state-of-the-art models on medical VQA and report generation benchmarks. |
Shadow-Activated Backdoor Attacks on Multimodal Large Language Models (2025.findings-acl)
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Ziyi Yin, Muchao Ye, Yuanpu Cao, Jiaqi Wang, Aofei Chang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma
| Challenge: | Existing backdoor attacks on Multimodal Large Language Models are less applicable to open-ended conversations with users. |
| Approach: | They propose a shadow-activated backdoor attack scenario where attackers inject malicious content into the responses of MLLMs when the responses explicitly relate to the shadowed object. |
| Outcome: | The proposed framework achieves the desired behaviors by constructing a poisoned dataset and implementing an attention-regularized tuning strategy. |
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for parameter-efficient fine-tuning are limited by computational and storage requirements. |
| Approach: | They propose a budget-guided iterative search strategy to disentangle binary module and rank dimension search spaces and early selection strategies based on parameter budgets. |
| Outcome: | The proposed method significantly improves search efficiency on public benchmarks. |