OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples

• URL: http://arxiv.org/abs/2307.11729v3
• Date: Sun, 18 Feb 2024 12:25:13 GMT
• Title: OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
• Authors: Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki
• Abstract summary: OUTFOX is a framework that improves the robustness of LLM-generated-text detectors by allowing both the detector and the attacker to consider each other's output. Experiments show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41.3 points F1-score. The detector shows a state-of-the-art detection performance: up to 96.9 points F1-score, beating existing detectors on non-attacked texts.
• Score: 44.118047780553006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have achieved human-level fluency in text generation, making it difficult to distinguish between human-written and LLM-generated texts. This poses a growing risk of misuse of LLMs and demands the development of detectors to identify LLM-generated texts. However, existing detectors lack robustness against attacks: they degrade detection accuracy by simply paraphrasing LLM-generated texts. Furthermore, a malicious user might attempt to deliberately evade the detectors based on detection results, but this has not been assumed in previous studies. In this paper, we propose OUTFOX, a framework that improves the robustness of LLM-generated-text detectors by allowing both the detector and the attacker to consider each other's output. In this framework, the attacker uses the detector's prediction labels as examples for in-context learning and adversarially generates essays that are harder to detect, while the detector uses the adversarially generated essays as examples for in-context learning to learn to detect essays from a strong attacker. Experiments in the domain of student essays show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41.3 points F1-score. Furthermore, the proposed detector shows a state-of-the-art detection performance: up to 96.9 points F1-score, beating existing detectors on non-attacked texts. Finally, the proposed attacker drastically degrades the performance of detectors by up to -57.0 points F1-score, massively outperforming the baseline paraphrasing method for evading detection.

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