Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework

Understand

Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement.

• However, their widespread use raises concerns about authorship, originality, and ethics, even potentially threatening scholarly integrity.
• Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts.
• To address these challenges, we propose a novel Multi-level Fine-grained Detection (MFD) framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features, while conducting sentence-level evaluations of lexicon, grammar, and syntax for comprehensive analysis.