Ghostbuster: Detecting Text Ghostwritten by Large Language Models (2024.naacl-long)
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| Challenge: | Ghostbuster is a system that passes documents through weaker language models, runs a structured search over possible combinations of their features, and trains a classifier on the selected features. |
| Approach: | They propose a method that passes documents through weaker language models, runs a structured search over possible combinations of their features, and trains a classifier on the selected features. |
| Outcome: | The proposed method outperforms existing detectors and a new baseline on student essays, creative writing, and news articles. |
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A Practical Examination of AI-Generated Text Detectors for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing methods to detect large language models are prone to misuse, such as generating fake news articles, facilitating academic plagiarism or spamming. |
| Approach: | They evaluate several popular detectors to evaluate their effectiveness against a range of domains, datasets, and models. |
| Outcome: | The proposed methods perform poorly in certain settings, with TPR@.01 as low as 0%. |
Detecting Machine-Generated Text: Techniques and Challenges (2024.acl-tutorials)
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| Challenge: | This tutorial focuses on machine-generated text and deepfakes. |
| Approach: | This tutorial aims to provide a comprehensive overview of text detection techniques . it will focus on machine-generated text and deepfakes . |
| Outcome: | This tutorial focuses on machine-generated text and deepfakes. |
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack (2024.lrec-main)
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
| Approach: | They propose a framework for a broader class of adversarial attacks to perform minor perturbations in machine-generated content to evade detection. |
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M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection (2024.eacl-long)
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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate fluent responses to user queries, but they are also susceptible to misuse in journalism, education, and academia. |
| Approach: | They propose a large-scale benchmark for machine-generated text detection that is a multi-generator, multi-domain, and multi-lingual corpus. |
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Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks, but their utilization carries inherent risks, including plagiarism and the dissemination of fake news. |
| Approach: | They propose to use a dataset to construct an AI-generated student essay that employs a range of text perturbation methods to evade detection. |
| Outcome: | The proposed methods evade detection and maintain quality of the generated essays while avoiding plagiarism and fake news. |
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking (2024.acl-demos)
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| Challenge: | MGTD methods are needed in many areas, such as prevention of disinformation spreading, plagiarism, impersonation and identity theft. |
| Approach: | They propose a framework for machine-generated text detection that integrates custom methods and evaluation datasets into existing frameworks. |
| Outcome: | The proposed framework simplifies the benchmarking of machine-generated text detection methods by easy integration of custom (new) methods and evaluation datasets. |
People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text (2025.acl-long)
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| Challenge: | Qualitative analysis of experts’ free-form explanations shows that while they rely heavily on specific lexical clues (‘AI vocabulary’), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity). |
| Approach: | They hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. |
| Outcome: | The annotators who frequently use LLMs for writing tasks outperform commercial and open-source detectors even without evasion tactics like paraphrasing and humanization. |
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
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| Challenge: | Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text. |
| Approach: | They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. |
| Outcome: | The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers. |
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors (2024.acl-long)
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| Challenge: | Recent research indicates that AI-text detection systems lack robustness and struggle to effectively differentiate perturbed texts. |
| Approach: | They propose to evaluate the robustness of current detection systems by using black-box text perturbation methods and adversarial learning experiments. |
| Outcome: | The proposed methods assess the robustness of current detection models across perturbation granularities and the impact of perturbation data augmentation on the robustity of AI-text detectors. |
On the Zero-Shot Generalization of Machine-Generated Text Detectors (2023.findings-emnlp)
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| Challenge: | rampant proliferation of large language models generates text indistinguishable from human-written language. |
| Approach: | They train neural detectors on outputs of a new generator and test their performance on held-out generators. |
| Outcome: | The proposed detectors can be built on training data from medium-sized models. |