@inproceedings{abassy-etal-2024-llm,
title = "{LLM}-{D}etect{AI}ve: a Tool for Fine-Grained Machine-Generated Text Detection",
author = "Abassy, Mervat and
Elozeiri, Kareem and
Aziz, Alexander and
Ta, Minh Ngoc and
Tomar, Raj Vardhan and
Adhikari, Bimarsha and
Ahmed, Saad El Dine and
Wang, Yuxia and
Mohammed Afzal, Osama and
Xie, Zhuohan and
Mansurov, Jonibek and
Artemova, Ekaterina and
Mikhailov, Vladislav and
Xing, Rui and
Geng, Jiahui and
Iqbal, Hasan and
Mujahid, Zain Muhammad and
Mahmoud, Tarek and
Tsvigun, Akim and
Aji, Alham Fikri and
Shelmanov, Artem and
Habash, Nizar and
Gurevych, Iryna and
Nakov, Preslav",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.35",
pages = "336--343",
abstract = "The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains.LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT{\_}bE7k8c.",
}
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<abstract>The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains.LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.</abstract>
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%0 Conference Proceedings
%T LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
%A Abassy, Mervat
%A Elozeiri, Kareem
%A Aziz, Alexander
%A Ta, Minh Ngoc
%A Tomar, Raj Vardhan
%A Adhikari, Bimarsha
%A Ahmed, Saad El Dine
%A Wang, Yuxia
%A Mohammed Afzal, Osama
%A Xie, Zhuohan
%A Mansurov, Jonibek
%A Artemova, Ekaterina
%A Mikhailov, Vladislav
%A Xing, Rui
%A Geng, Jiahui
%A Iqbal, Hasan
%A Mujahid, Zain Muhammad
%A Mahmoud, Tarek
%A Tsvigun, Akim
%A Aji, Alham Fikri
%A Shelmanov, Artem
%A Habash, Nizar
%A Gurevych, Iryna
%A Nakov, Preslav
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F abassy-etal-2024-llm
%X The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains.LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.
%U https://aclanthology.org/2024.emnlp-demo.35
%P 336-343
Markdown (Informal)
[LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection](https://aclanthology.org/2024.emnlp-demo.35) (Abassy et al., EMNLP 2024)
ACL
- Mervat Abassy, Kareem Elozeiri, Alexander Aziz, Minh Ngoc Ta, Raj Vardhan Tomar, Bimarsha Adhikari, Saad El Dine Ahmed, Yuxia Wang, Osama Mohammed Afzal, Zhuohan Xie, Jonibek Mansurov, Ekaterina Artemova, Vladislav Mikhailov, Rui Xing, Jiahui Geng, Hasan Iqbal, Zain Muhammad Mujahid, Tarek Mahmoud, Akim Tsvigun, et al.. 2024. LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 336–343, Miami, Florida, USA. Association for Computational Linguistics.