Papers with AIGT
DART: An AIGT Detector using AMR of Rephrased Text (2025.naacl-short)
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| Challenge: | Existing methods for detecting AIGTs focus on probabilistic features, causing problems . performance of black-box detectors is low, and it is difficult to detect black- box models . |
| Approach: | They propose a detector that can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
| Outcome: | The proposed method can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
SeqXGPT: Sentence-Level AI-Generated Text Detection (2023.emnlp-main)
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| Challenge: | Existing methods for sentence-level AIGT detection are weak . large language models (LLMs) can generate human-like content . |
| Approach: | They propose a sentence-level AIGT detection challenge using LLMs as log probability lists . they propose 'check' GPT' method that uses log probability list features to detect AIGT . |
| Outcome: | The proposed method surpasses baseline methods in sentence- and document-level detection challenges. |
Are AI-Generated Text Detectors Robust to Adversarial Perturbations? (2024.acl-long)
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| Challenge: | Existing detectors for AI-generated text lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text. |
| Approach: | They propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations. |
| Outcome: | The proposed detector outperforms baseline methods on four datasets and is more generalizable in cross-domain, cross-genre, and mixed-source scenarios. |
Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations (2025.naacl-long)
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| Challenge: | Existing methods focus on model generalization or focus on robustness. |
| Approach: | They propose a model-based AIGT detection method that can be generalized and robust under two adversarial attacks. |
| Outcome: | The proposed method outperforms state-of-the-art methods for generalization and robustness under two text adversarial attacks. |
AIGT: AI Generative Table Based on Prompt (2025.coling-main)
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| Challenge: | Tabular data is an essential resource for many fields, but current methods do not fully utilize the rich information available in tables. |
| Approach: | They propose a method that utilizes metadata information to generate tabular data . they propose long-token partitioning algorithms that enable AIGT to model tables of any scale . |
| Outcome: | The proposed approach achieves state-of-the-art on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system. |
MASH: Evading Black-Box AI-Generated Text Detectors via Style Humanization (2026.findings-acl)
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| Challenge: | Existing detection methods rely on white-box assumptions or require prohibitively high computational and interaction costs, rendering them ineffective under practical black-box scenarios. |
| Approach: | They propose a framework that evades black-box detection methods based on style transfer by using style-injection supervised fine-tuning and direct preference optimization to shape distributions of AI-generated texts to resemble those of human-written texts. |
| Outcome: | The proposed framework achieves an average Attack Success Rate (ASR) of 92%, surpassing the strongest baselines by an average of 24% while maintaining superior linguistic quality. |