厘清大模型生成文本的定义边界，揭示检测难题的本质。
On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?
- 提出系统性定义大模型生成文本的框架，涵盖多种输出形式。
- 实证发现现有检测器在真实场景中表现不稳定，结果不可靠。
- 适合关注生成内容可信度与检测局限性的研究者阅读。
随着大型语言模型（LLMs）的广泛应用，许多研究者转向检测其生成的文本。然而，对目标对象“LLM生成文本”的定义缺乏一致性和精确性。使用场景差异和模型多样性进一步增加了检测难度。通常被视为检测目标的文本仅代表了LLM可能产生的子集。人类对LLM输出的修改，以及模型对用户产生的微妙影响，正模糊生成文本与人工写作之间的界限。现有基准和评估方法未能充分涵盖实际检测应用中的各种条件，导致检测器的数值结果常被误解，其意义逐渐减弱。因此，检测器仅在特定条件下仍有用，但其结果应仅作为参考，而非决定性指标。
原文摘要 · Abstract (English)
With the widespread use of large language models (LLMs), many researchers have turned their attention to detecting text generated by them. However, there is no consistent or precise definition of their target, namely "LLM-generated text". Differences in usage scenarios and the diversity of LLMs further increase the difficulty of detection. What is commonly regarded as the detecting target usually represents only a subset of the text that LLMs can potentially produce. Human edits to LLM outputs, together with the subtle influences that LLMs exert on their users, are blurring the line between LLM-generated and human-written text. Existing benchmarks and evaluation approaches do not adequately address the various conditions in real-world detector applications. Hence, the numerical results of detectors are often misunderstood, and their significance is diminishing. Therefore, detectors remain useful under specific conditions, but their results should be interpreted only as references rather than decisive indicators.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。