The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content. The LLM-generated text detection aims to discern if a piece of text was produced by an LLM, which is essentially a binary classification task. The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, zero-shot methods, fine-turning LMs methods, adversarial learning methods, LLMs as detectors, and human-assisted methods. In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research. We also delve into prevalent datasets, elucidating their limitations and developmental requirements. Furthermore, we analyze various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, and data ambiguity. Conclusively, we highlight interesting directions for future research in LLM-generated text detection to advance the implementation of responsible artificial intelligence (AI). Our aim with this survey is to provide a clear and comprehensive introduction for newcomers while also offering seasoned researchers a valuable update in the field of LLM-generated text detection. The useful resources are publicly available at: https://github.com/NLP2CT/LLM-generated-Text-Detection.

翻译：大语言模型（LLMs）在理解、遵循并生成复杂语言方面展现出的强大能力，使得LLM生成文本以惊人的速度涌入日常生活的各个领域，并被人类广泛接受。随着LLM规模的持续扩大，迫切需要开发能够检测LLM生成文本的检测器。这对于减轻LLM的潜在滥用，并保护艺术表达和社交网络等领域免受LLM生成内容的有害影响至关重要。LLM生成文本检测旨在判断一段文本是否由LLM生成，本质上是一个二分类任务。近年来，在水印技术、零样本方法、微调LM方法、对抗学习方法、以LLM作为检测器以及人工辅助方法等创新的推动下，检测器技术取得了显著进展。本综述汇集了该领域近期研究突破，强调加强检测器研究的紧迫性。我们还深入探讨了主流数据集，阐明了它们的局限性和发展需求。此外，我们分析了多种LLM生成文本检测范式，揭示了诸如分布外问题、潜在攻击和数据歧义等挑战。最后，我们指出了LLM生成文本检测领域未来研究的有趣方向，以推动负责任的人工智能（AI）的实施。本综述旨在为新手提供清晰全面的入门介绍，同时为资深研究者提供LLM生成文本检测领域有价值的最新动态。相关资源已公开于：https://github.com/NLP2CT/LLM-generated-Text-Detection。