With the advent of large language models (LLMs), it has become common practice for users to draft text and utilize LLMs to enhance its quality through paraphrasing. However, this process can sometimes result in the loss or distortion of the original intended meaning. Due to the human-like quality of LLM-generated text, traditional detection methods often fail, particularly when text is paraphrased to closely mimic original content. In response to these challenges, we propose a novel approach named SearchLLM, designed to identify LLM-paraphrased text by leveraging search engine capabilities to locate potential original text sources. By analyzing similarities between the input and regenerated versions of candidate sources, SearchLLM effectively distinguishes LLM-paraphrased content. SearchLLM is designed as a proxy layer, allowing seamless integration with existing detectors to enhance their performance. Experimental results across various LLMs demonstrate that SearchLLM consistently enhances the accuracy of recent detectors in detecting LLM-paraphrased text that closely mimics original content. Furthermore, SearchLLM also helps the detectors prevent paraphrasing attacks.

翻译：随着大语言模型（LLMs）的出现，用户起草文本并利用LLM通过释义来提升其质量已成为常见做法。然而，这一过程有时可能导致原始预期含义的丢失或扭曲。由于LLM生成文本具有类人质量，传统检测方法常常失效，尤其是在文本经过释义以紧密模仿原始内容的情况下。针对这些挑战，我们提出了一种名为SearchLLM的新方法，旨在通过利用搜索引擎功能定位潜在的原始文本源来识别LLM释义文本。通过分析输入文本与候选源再生版本之间的相似性，SearchLLM能有效区分LLM释义内容。SearchLLM被设计为一个代理层，可与现有检测器无缝集成以提升其性能。在不同LLM上的实验结果表明，SearchLLM能持续提升最新检测器在检测紧密模仿原始内容的LLM释义文本时的准确性。此外，SearchLLM还有助于检测器防范释义攻击。