Papers with MGTs
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection (2024.acl-long)
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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Large Language Models (LLMs) have brought an unprecedented surge in machine-generated text (MGT) societal implications are posed by their potential misuse and lack of training data. |
| Approach: | They propose a benchmark to detect machine-generated text in multiple languages . they use multi-domain and multi-generator corpus to identify which model generated the text . |
| Outcome: | The proposed benchmark compares a multilingual, multi-domain and multi-generator corpus of MGTs with human-generated content. |
Beemo: Benchmark of Expert-edited Machine-generated Outputs (2025.naacl-long)
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Ekaterina Artemova, Jason S Lucas, Saranya Venkatraman, Jooyoung Lee, Sergei Tilga, Adaku Uchendu, Vladislav Mikhailov
| Challenge: | Existing benchmarks for machine-generated texts (MGTs) include single-author texts (human-written and machine-generated). |
| Approach: | They propose to benchmark machine-generated outputs (Beemo) which includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases. |
| Outcome: | The proposed benchmark includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases, ranging from creative writing to summarization. |
Authorship Obfuscation in Multilingual Machine-Generated Text Detection (2024.findings-emnlp)
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Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | Recent advances in Language Modeling have birthed Large Language Models (LLMs), which exhibit significant improvements, including the ability to generate texts easily misconstrued as humanwritten. |
| Approach: | They compare authorship obfuscation methods against machine-generated text (MGT) in 11 languages and analyze their performance against 37 well-known AO methods. |
| Outcome: | The proposed methods can cause evasion of detection in all languages, with homoglyph attacks particularly successful. |
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
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| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |