  Large Language Models (LLMs) have revolutionized the field of Natural
Language Generation (NLG) by demonstrating an impressive ability to generate
human-like text. However, their widespread usage introduces challenges that
necessitate thoughtful examination, ethical scrutiny, and responsible
practices. In this study, we delve into these challenges, explore existing
strategies for mitigating them, with a particular emphasis on identifying
AI-generated text as the ultimate solution. Additionally, we assess the
feasibility of detection from a theoretical perspective and propose novel
research directions to address the current limitations in this domain.
