Large Language Models (LLMs) have become increasingly proficient in generating human-like text, yet their widespread deployment raises significant concerns, including disseminating fake information, privacy violations, and academic dishonesty. Detecting LLM-generated text is vital for mitigating these risks and is often framed as a binary classification task. Although zero-shot textual analysis methods have gained popularity due to their generalizability, they face key challenges: (1) limited ability to capture holistic and granular consistency and (2) insufficiently comprehensive textual analysis, particularly regarding syntax and lexical patterns. To address these problems, we propose a novel Multi-faceted Granular Analysis Framework (MFGAF), which leverages Rewriting Concordance and Completive Concordance to detect LLM-generated text through multi-granular textual dissection. Specifically, MFGAF is designed with two perspectives, Rewriting and Completion, to comprehensively capture global and local LLM-generated features. Additionally, for each perspective, a Multi-Granular Textual Dissection mechanism is constructed to thoroughly analyze LLM-generated text. Finally, MFGAF leverages LLMs to achieve adaptive integration of text analysis results and reflect on the correctness of these results. Our method achieves an average F1 improvement of 3.06% compared to the best baselines, demonstrating its robustness and effectiveness through extensive evaluations.
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MFGAF: Multi-faceted Granular Analysis Framework for LLM-Generated Text Detection
Semantic Scholar · Computer Science · 2025
Abstract
Large Language Models (LLMs) have become increasingly proficient in generating human-like text, yet their widespread deployment raises significant concerns, including disseminating fake information, privacy violations, and academic dishonesty. Detecting LLM-generated text is vital for mitigating these risks and is often framed as a binary classification task. Although zero-shot textual analysis methods have gained popularity due to their generalizability, they face key challenges: (1) limited ability to capture holistic and granular consistency and (2) insufficiently comprehensive textual analysis, particularly regarding syntax and lexical patterns. To address these problems, we propose a novel Multi-faceted Granular Analysis Framework (MFGAF), which leverages Rewriting Concordance and Completive Concordance to detect LLM-generated text through multi-granular textual dissection. Specifically, MFGAF is designed with two perspectives, Rewriting and Completion, to comprehensively capture global and local LLM-generated features. Additionally, for each perspective, a Multi-Granular Textual Dissection mechanism is constructed to thoroughly analyze LLM-generated text. Finally, MFGAF leverages LLMs to achieve adaptive integration of text analysis results and reflect on the correctness of these results. Our method achieves an average F1 improvement of 3.06% compared to the best baselines, demonstrating its robustness and effectiveness through extensive evaluations.