Abassy, Mervat ; Elozeiri, Kareem ; Aziz, Alexander ; Ta, Minh Ngoc ; Tomar, Raj Vardhan ; Adhikari, Bimarsha ; Ahmed, Saad El Dine ; Wang, Yuxia ; Afzal, Osama Mohammed ; Xie, Zhuohan ; Mansurov, Jonibek ; Artemova, Ekaterina ; Mikhailov, Vladislav ; Xing, rui ; Geng, Jiahui ; Iqbal, Hasan ; Mujahid, Zain Muhammad ; Mahmoud, Tarek ; Tsvigun, Akim ; Aji, Alham Fikri ; Shelmanov, Artem ; Habash, Nizar ; Gurevych, Iryna ; Nakov, Preslav (2024)
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection.
29th Conference on Empirical Methods in Natural Language Processing. Miami, USA (12.11.2024 - 16.11.2024)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Abassy, Mervat ; Elozeiri, Kareem ; Aziz, Alexander ; Ta, Minh Ngoc ; Tomar, Raj Vardhan ; Adhikari, Bimarsha ; Ahmed, Saad El Dine ; Wang, Yuxia ; Afzal, Osama Mohammed ; Xie, Zhuohan ; Mansurov, Jonibek ; Artemova, Ekaterina ; Mikhailov, Vladislav ; Xing, rui ; Geng, Jiahui ; Iqbal, Hasan ; Mujahid, Zain Muhammad ; Mahmoud, Tarek ; Tsvigun, Akim ; Aji, Alham Fikri ; Shelmanov, Artem ; Habash, Nizar ; Gurevych, Iryna ; Nakov, Preslav |
| Art des Eintrags: | Bibliographie |
| Titel: | LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection |
| Sprache: | Englisch |
| Publikationsjahr: | November 2024 |
| Verlag: | ACL |
| Buchtitel: | EMNLP 2024: The 2024 Conference on Empirical Methods in Natural Language Processing: Proceedings of System Demonstrations |
| Veranstaltungstitel: | 29th Conference on Empirical Methods in Natural Language Processing |
| Veranstaltungsort: | Miami, USA |
| Veranstaltungsdatum: | 12.11.2024 - 16.11.2024 |
| URL / URN: | https://aclanthology.org/2024.emnlp-demo.35/ |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 17 Dez 2024 11:32 |
| Letzte Änderung: | 17 Dez 2024 11:32 |
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