Papers by Dehan Kong
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)
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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji
| Challenge: | Existing models of robustness evaluation are incomprehensive, impractical, and invalid . |
| Approach: | They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks. |
| Outcome: | The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol. |
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning (2023.findings-acl)
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| Challenge: | Large language models (LLMs) have shown great potential for in-context learning, but their robustness and performance on downstream tasks remains limited. |
| Approach: | They propose to examine the reliance of LLMs on shortcuts or spurious correlations within prompts for downstream tasks and find larger models are more likely to utilize shortcuts in prompts during inference. |
| Outcome: | The proposed model is “lazy learner” and more likely to use shortcuts in prompts during inference. |
Adversarial Text Generation by Search and Learning (2023.findings-emnlp)
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Guoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
| Challenge: | Existing text generation methods only use heuristic replacement strategies or language models to generate replacement words at the word level. |
| Approach: | They propose a search and learning framework for Adversarial Text Generation by Search and Learning to evaluate the robustness of natural language processing models. |
| Outcome: | The proposed methods are significantly superior to the most advanced methods in terms of attack efficiency and adversarial text quality. |
Multiple Instance Learning for Offensive Language Detection (2022.findings-emnlp)
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| Challenge: | Existing approaches to detect offensive content are expensive and require massive manual effort. |
| Approach: | They propose an approach capable of utilizing the bag-level labeled data for offensive language detection by an annotation-based model. |
| Outcome: | The proposed model can detect offensive language on both bag-level and sentence level. |