01882nas a2200265   4500000000100000000000100001008004100002260001200043653002900055653001700084653002600101653002200127653001100149100002000160700001900180700001700199700002400216700002000240245010600260856005800366300000900424490001300433520115600446022001401602       9998                            d  c12/202410aGenerated-Text Detection10aAI-Detection10aLarge Language Models10aLiterature Review10aSurvey1 aSerena Fariello1 aGiuseppe Fenza1 aFlavia Forte1 aMariacristina Gallo1 aMartina Marotta00aDistinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection  uhttps://www.ijimai.org/journal/bibcite/reference/3523  a1-130 vIn press3 aThe 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.  a1989-1660