Papers by Jiyang Zhang
Impact of Evaluation Methodologies on Code Summarization (2022.acl-long)
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| Challenge: | Existing evaluation methodologies for code summarization tasks do not consider timestamps of code and comments. |
| Approach: | They propose a time-segmented evaluation methodology for code summarization that considers timestamps of code and comments during evaluation. |
| Outcome: | The proposed evaluation methodology compares with other evaluation methodologies that have been widely used. |
On the Robustness of Editing Large Language Models (2024.emnlp-main)
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| Challenge: | Existing studies have exhibited impressive success and significant potential. |
| Approach: | They propose to modify the knowledge memory with minimum computational cost while preserving the performance on the retained knowledge. |
| Outcome: | The proposed methods avoid retraining to update the model parameters and have demonstrated promising performance and efficiency. |
MEGen: Generative Backdoor into Large Language Models via Model Editing (2025.findings-acl)
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| Challenge: | Existing methods for training large language models are limited to yes-or-no discriminative tasks, leading users to underestimate the potential risks. |
| Approach: | They propose an editing-based generative backdoor that expands the backdoor to generative tasks in a unified format of any text-to-any text. |
| Outcome: | The proposed model achieves high attack success rate by adjusting only a small set of local parameters with few-shot samples. |