Papers with CoPA
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors (2025.emnlp-main)
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Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
| Challenge: | Existing methods to detect large language models (LLMs) generated for plagiarism use paraphrases to rewrite them to evade detection. |
| Approach: | They propose a training-free method that effectively fools text detectors using off-the-shelf LLMs by rewriting them to evade detection. |
| Outcome: | The proposed method deceives text detectors using off-the-shelf LLMs by rewriting them to produce human-like sentences that are less discernible by detectors. |
CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors (2026.findings-acl)
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| Challenge: | Existing LLMs rely on surface-level similarity or manual heuristics to evaluate personalization . Existing evaluation protocols for personalization are lacking sufficient data-driven validation. |
| Approach: | They propose a benchmark to assess personalization by mining CIPDs to quantify individual preferences. |
| Outcome: | The proposed benchmark provides a more comprehensive and discriminative standard than generic metrics. |