Papers by Ziyi Yin
Can Factual Opinions Be Edited (Manipulated) in Large Language Models? (2026.acl-long)
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| Challenge: | Existing methods for factual opinion editing focus on atomic facts, ignoring the risks associated with factual opinions. |
| Approach: | They propose a method that achieves opinion–evidence alignment without relying on explicit instructions to edit factual opinions. |
| Outcome: | The proposed method achieves opinion–evidence alignment without relying on explicit instructions. |
Language Reconstruction with Brain Predictive Coding from fMRI Data (2026.acl-long)
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| Challenge: | Existing studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. |
| Approach: | They propose to use FMRI-to-text decoding with Predictive coding to generate a main network and a side network to generate brain predictive representations from related regions of interest. |
| Outcome: | The proposed model outperforms current decoding models on several evaluation metrics on two naturalistic language comprehension fMRI datasets. |
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. |
ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)
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| Challenge: | Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice. |
| Approach: | They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment. |
| Outcome: | The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent. |
Hierarchical Pretraining on Multimodal Electronic Health Records (2023.emnlp-main)
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| Challenge: | Existing pretraining models on EHR data are too specific, limiting their transferability. |
| Approach: | They propose a general, unified pretraining framework for hierarchically multimodal EHR data that can be used to train models on a large dataset before fine-tuning it on 'upstream' tasks. |
| Outcome: | The proposed model performs on eight downstream tasks spanning three levels and compares with baselines on 18 different tasks. |