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.

大型语言模型（LLMs）在自然语言生成（NLG）领域引发了一场革命，展示出生成类似人类文本的惊人能力。然而，它们的广泛使用引入了挑战，必须经过深入研究、伦理审查和负责任的实践来加以解决。本研究深入探讨了这些挑战，探索了现有的缓解策略，尤其强调将 AI 生成的文本作为最终解决方案。此外，我们从理论角度评估了检测的可行性，并提出了解决当前领域限制的新的研究方向。