SearchLLM: Detecting LLM Paraphrased Text by Measuring the Similarity with Regeneration of the Candidate Source via Search Engine

2026-01-23

AI Summary

This work addresses the challenge of detecting high-fidelity paraphrased text generated by large language models (LLMs), which preserves semantic content so effectively that conventional detection methods often fail. To tackle this issue, the authors propose SearchLLM, a novel approach that integrates a search engine with a text regeneration mechanism. Specifically, SearchLLM retrieves candidate source texts, regenerates their LLM-paraphrased versions, and compares these against the input text to assess similarity. Designed as a plug-in proxy layer, SearchLLM seamlessly enhances existing detectors without requiring architectural modifications. Experimental results demonstrate that this method significantly improves detection accuracy across diverse LLM-generated paraphrasing datasets, thereby substantially increasing robustness against semantically faithful paraphrasing attacks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• LLM paraphrasing
• text detection
• paraphrasing attack
• original source identification
• AI-generated text

Innovation

• SearchLLM
• LLM-paraphrased text detection
• search engine regeneration
• paraphrasing attack defense
• proxy-layer integration