IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector

• URL: http://arxiv.org/abs/2502.15902v1
• Date: Fri, 21 Feb 2025 19:41:32 GMT
• Title: IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector
• Authors: Zheng Chen, Yushi Feng, Changyang He, Yue Deng, Hongxi Pu, Bo Li,
• Abstract summary: Large Language Models (LLMs) have attained human-level fluency in text generation, which complicates the distinguishing between human-written and LLM-generated texts.<n>Existing detectors exhibit poor robustness on out-of-distribution data and attacked data, which is critical for real-world scenarios.<n>We propose IPAD (Inverse Prompt for AI Detection), a novel framework consisting of a Prompt that identifies predicted prompts that could have generated the input text, and a Distinguisher that examines how well the input texts align with the predicted prompts.
• Score: 11.112793289424886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have attained human-level fluency in text generation, which complicates the distinguishing between human-written and LLM-generated texts. This increases the risk of misuse and highlights the need for reliable detectors. Yet, existing detectors exhibit poor robustness on out-of-distribution (OOD) data and attacked data, which is critical for real-world scenarios. Also, they struggle to provide explainable evidence to support their decisions, thus undermining the reliability. In light of these challenges, we propose IPAD (Inverse Prompt for AI Detection), a novel framework consisting of a Prompt Inverter that identifies predicted prompts that could have generated the input text, and a Distinguisher that examines how well the input texts align with the predicted prompts. We develop and examine two versions of Distinguishers. Empirical evaluations demonstrate that both Distinguishers perform significantly better than the baseline methods, with version2 outperforming baselines by 9.73% on in-distribution data (F1-score) and 12.65% on OOD data (AUROC). Furthermore, a user study is conducted to illustrate that IPAD enhances the AI detection trustworthiness by allowing users to directly examine the decision-making evidence, which provides interpretable support for its state-of-the-art detection results.

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