Automatic Detection of Machine Generated Text: A Critical Survey (2020.coling-main)
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| Challenge: | Current text generative models excel in producing text that matches the style of human language reasonably well. |
| Approach: | They conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area. |
| Outcome: | The proposed detectors can distinguish between human and text generated by the model and can be used to generate fake news and fake product reviews. |
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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. |
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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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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. |
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Automatic Detection of Generated Text is Easiest when Humans are Fooled (2020.acl-main)
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| Challenge: | Recent advances in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. |
| Approach: | They compare decoding methods with popular sampling-based decoding strategies . they show that multi-sentence excerpts can fool expert human raters over 30% of the time . |
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Adapting Fake News Detection to the Era of Large Language Models (2024.findings-naacl)
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| Challenge: | a gap exists in understanding the interplay between machine-paraphrased real news, machine-generated fake news, and human-written real news . false information is easier to generate but harder to detect due to the bias of detectors against machine-generated texts . |
| Approach: | They propose a strategy to adapt fake news detectors to the era of large language models and AI-driven content creation . |
| Outcome: | The proposed detectors perform well on human-written articles but not vice versa . the proposed detector should be trained on datasets with lower machine-generated news ratio than the test set . |
GLTR: Statistical Detection and Visualization of Generated Text (P19-3)
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| Challenge: | GLTR is a tool to detect generated text that can be used by non-experts. |
| Approach: | They propose a tool to detect generated text using a set of statistical methods that can be used by non-experts. |
| Outcome: | The proposed method improves detection rate of fake text from 54% to 72% without training. |
Identifying Bias in Machine-generated Text Detection (2026.acl-long)
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| Challenge: | a growing number of generative AI systems are detecting text generated by a model or written by . humans perform poorly at the detection task, but show no significant biases on the studied attributes. |
| Approach: | They examine gender, race/ethnicity, English-language learner status, and economic status . they find several models tend to classify disadvantaged groups as machine-generated . |
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Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)
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| Challenge: | Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions. |
| Approach: | They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features. |
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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. |