TY - JOUR
KW - Generated-Text Detection
KW - AI-Detection
KW - Large Language Models
KW - Literature Review
KW - Survey
AU - Serena Fariello
AU - Giuseppe Fenza
AU - Flavia Forte
AU - Mariacristina Gallo
AU - Martina Marotta
AB - The rise of Large Language Models (LLMs) has dramatically altered the generation and spreading of textual content. This advancement offers benefits in various domains, including medicine, education, law, coding, and journalism, but also has negative implications, mainly related to ethical concerns. Preventing measures to mitigate negative implications pass through solutions that distinguish machine-generated text from humanwritten text. This study aims to provide a comprehensive review of existing literature for detecting LLMgenerated texts. Emerging techniques are categorized into five categories: watermarking, feature-based, neural-based, hybrid, and human-aided methods. For each introduced category, strengths and limitations are discussed, providing insights into their effectiveness and potential for future improvements. Moreover, available datasets and tools are introduced. Results demonstrate that, despite the good delimited performance, the multitude of languages to recognize, hybrid texts, the continuous improvement of algorithms for text generation and the lack of regulation require additional efforts for efficient detection.
IS - In press
M1 - In press
N2 - The rise of Large Language Models (LLMs) has dramatically altered the generation and spreading of textual content. This advancement offers benefits in various domains, including medicine, education, law, coding, and journalism, but also has negative implications, mainly related to ethical concerns. Preventing measures to mitigate negative implications pass through solutions that distinguish machine-generated text from humanwritten text. This study aims to provide a comprehensive review of existing literature for detecting LLMgenerated texts. Emerging techniques are categorized into five categories: watermarking, feature-based, neural-based, hybrid, and human-aided methods. For each introduced category, strengths and limitations are discussed, providing insights into their effectiveness and potential for future improvements. Moreover, available datasets and tools are introduced. Results demonstrate that, despite the good delimited performance, the multitude of languages to recognize, hybrid texts, the continuous improvement of algorithms for text generation and the lack of regulation require additional efforts for efficient detection.
PY - 9998
SE - 1
SP - 1
EP - 13
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Distinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection
UR - https://www.ijimai.org/journal/bibcite/reference/3523
VL - In press
SN - 1989-1660
ER -