SimLLM: Detecting Sentences Generated by Large Language Models Using Similarity between the Generation and its Re-generation (2024.emnlp-main)
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| Challenge: | Prior studies have detected the generation of non-analogous text with substantial differences between original and generated content. |
| Approach: | They propose a method to detect analogous machine-generated sentences that closely mimic human-written ones by estimating the similarity between an input sentence and its generated counterpart. |
| Outcome: | The proposed approach outperforms existing methods in academic dishonesty, spam dissemination, and misinformation propagation. |
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Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
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| Challenge: | Large Language Models (LLMs) generate human-like text, but have ethical and misuse concerns. |
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