SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text

• URL: http://arxiv.org/abs/2411.12764v1
• Date: Sun, 17 Nov 2024 20:13:30 GMT
• Title: SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
• Authors: Weiqing He, Bojian Hou, Tianqi Shang, Davoud Ataee Tarzanagh, Qi Long, Li Shen,
• Abstract summary: We present a novel semantic-enhanced framework for detecting large language models (LLMs)-generated text (SEFD) Our framework improves upon existing detection methods by systematically integrating retrieval-based techniques with traditional detectors. We showcase the effectiveness of our approach in sequential text scenarios common in real-world applications, such as online forums and Q&A platforms.
• Score: 12.639191350218528
• License:
• Abstract: The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techniques that often evade existing detection methods. To address this challenge, we present a novel semantic-enhanced framework for detecting LLM-generated text (SEFD) that leverages a retrieval-based mechanism to fully utilize text semantics. Our framework improves upon existing detection methods by systematically integrating retrieval-based techniques with traditional detectors, employing a carefully curated retrieval mechanism that strikes a balance between comprehensive coverage and computational efficiency. We showcase the effectiveness of our approach in sequential text scenarios common in real-world applications, such as online forums and Q\&A platforms. Through comprehensive experiments across various LLM-generated texts and detection methods, we demonstrate that our framework substantially enhances detection accuracy in paraphrasing scenarios while maintaining robustness for standard LLM-generated content.

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