Papers by Collin Zhang
Adversarial Decoding: Generating Readable Documents for Adversarial Objectives (2026.findings-eacl)
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| Challenge: | Existing methods for generating adversarial documents produce gibberish that is easy to detect and filter out. |
| Approach: | They propose a generic text generation technique that produces readable adversarial documents . they demonstrate that adversarials can be used for different objectives . |
| Outcome: | The proposed technique outperforms existing methods while producing readable documents for adversarial objectives. |
Extracting Prompts by Inverting LLM Outputs (2024.emnlp-main)
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| Challenge: | Unlike previous methods, output2prompt only needs outputs of normal user queries. |
| Approach: | They propose a black-box method that extracts the model's prompt without accessing its logits and without adversarial or jailbreaking queries. |
| Outcome: | The proposed method extracts the prompt that generated the outputs without accessing the model's logits and without adversarial or jailbreaking queries. |
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention (2026.acl-long)
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Yifan Zhang, Chen Huang, Yueke Zhang, Jiahao Zhang, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang
| Challenge: | Code Language Models learn attention based on statistical input-output token correlations. |
| Approach: | They propose a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes. |
| Outcome: | The proposed model outperforms baselines in three languages, with gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization. |