Papers by Shiqing Zhang
NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models (2023.acl-long)
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| Challenge: | Existing backdoor attacks against prompt-based learning involve injecting back doors into embedding layers or word embedders. |
| Approach: | They propose a backdoor attack against prompt-based learning that injects backdoors into embedding layers or word embeddable vectors. |
| Outcome: | The proposed backdoor attack outperforms two state-of-the-art models on six NLP tasks and three prompting strategies. |
False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models (2026.acl-long)
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| Challenge: | Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain largely unexplored. |
| Approach: | They propose to use ASCII-based emoticons to perform unintended actions in large language models (LLMs) This vulnerability is pervasive, with an average confusion ratio exceeding 38%, and 90% of confused responses yield 'silent failures' authors call on the community to recognize this emerging vulnerability and develop effective mitigation methods to uphold the safety and reliability of human-LLM interactions. |
| Outcome: | The proposed framework exploits emoticon semantic confusion in six LLMs and demonstrates that existing prompt-based mitigations are ineffective. |
GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning (2026.findings-acl)
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Yule Xie, Jiaxin Ding, Cheng Deng, Shiqing Gao, Junran Zhang, Sibo Zhang, Zeyuan Wang, Ke Wu, Xin Ding, Luoyi Fu, Meng Jin, Xinbing Wang
| Challenge: | Recent advances in large language models have demonstrated RL's substantial capacity to enhance multi-step reasoning beyond what supervised instruction tuning achieves. |
| Approach: | They propose a framework that converts multimodal questions into descriptive text . they propose RL-enhanced geoscience reasoning that can be fine-tuned to a text-only level . |
| Outcome: | The proposed framework improves accuracy and accuracy on multimodal questions while preserving answerability and difficulty. |
Integrating Representation Subspace Mapping with Unimodal Auxiliary Loss for Attention-based Multimodal Emotion Recognition (2024.lrec-main)
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Xulong Du, Xingnan Zhang, Dandan Wang, Yingying Xu, Zhiyuan Wu, Shiqing Zhang, Xiaoming Zhao, Jun Yu, Liangliang Lou
| Challenge: | Existing methods to identify emotions rely on a large modality gap in their representations . |
| Approach: | They propose a representation subspace mapping module that maps each modality into two distinct subspaces and a cross-modality attention module that leverages auxiliary loss to remove the noise unrelated to emotion classification. |
| Outcome: | The proposed approach achieves superior performance to state-of-the-art MER methods on the IEMOCAP and MSP-Improv datasets. |
The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation. |
| Approach: | They propose to use a dataset to investigate a new type of bias in Large Language Models for code generation, provider bias, to determine whether the model favors specific providers. |
| Outcome: | The proposed model favors services from Google and Amazon, but without explicit directives, and can modify input code to incorporate their preferred providers without user requests. |