AI 检测器，也称为 AI 写作检测器或 AI 内容检测器，是专门用于识别部分或全部由人工智能 （AI） 模型（如 ChatGPT）生成的文本的专用工具。这些检测器有多种用途，从验证书面内容的真实性到过滤掉虚假的产品评论和垃圾邮件。在这篇博文中，我们将探讨 AI 检测器背后的原理、它们当前的可靠性以及它们可以应用的场景。
How Do AI Detectors Work? AI 探测器如何工作？
AI detectors typically rely on language models that resemble the ones used by the AI writing tools they aim to detect. The core principle involves the model assessing a piece of text to determine if it resembles something it would generate itself. If the answer is affirmative, it suggests that the text may be AI-generated.
AI 检测器通常依赖于类似于它们旨在检测的 AI 写作工具所使用的语言模型。核心原则涉及模型评估一段文本，以确定它是否类似于它自己生成的东西。如果答案是肯定的，则表明该文本可能是人工智能生成的。
AI detectors focus on two key variables within a text: perplexity and burstiness. Lower values of these variables indicate a higher likelihood that the text is AI-generated. Let's clarify what these terms mean:
AI 检测器专注于文本中的两个关键变量：困惑性和突发性。这些变量的值越低，表示文本由 AI 生成的可能性越高。让我们澄清一下这些术语的含义：
Perplexity: 困惑：
Perplexity measures how unpredictable a text is, gauging its potential to confuse or perplex an average reader. In other words, it quantifies how sensical and natural the text reads.
困惑度衡量文本的不可预测性，衡量其使普通读者感到困惑或困惑的可能性。换句话说，它量化了文本阅读的感性和自然程度。
- AI language models aim to produce texts with low perplexity, as they are more likely to make sense and read smoothly, but they are also more predictable.
人工智能语言模型旨在生成具有低困惑度的文本，因为它们更有可能有意义和流畅地阅读，但它们也更可预测。 - Human writing tends to exhibit higher perplexity due to more creative language choices, albeit with occasional typos.
由于更具创造性的语言选择，人类写作往往会表现出更高的困惑，尽管偶尔会有错别字。
Language models operate by predicting the next word in a sentence, selecting the most fitting option. For example, in the sentence "I couldn't get to sleep last...," different continuations have varying degrees of plausibility.
语言模型通过预测句子中的下一个单词来运行，选择最合适的选项。例如，在句子“我最后睡不着......”中，不同的延续具有不同程度的合理性。
Low perplexity is indicative of AI-generated text.
低困惑度表示 AI 生成的文本。
Burstiness: 突发性：
Burstiness measures the variation in sentence structure and length, akin to perplexity but focused on sentences rather than individual words.
突发性衡量的是句子结构和长度的变化，类似于困惑，但侧重于句子而不是单个单词。
- Texts with minimal variation in sentence structure and length have low burstiness..
句子结构和长度变化最小的文本具有较低的突发性。 - Texts with diverse structures and lengths exhibit high burstiness.
具有不同结构和长度的文本表现出很高的突发性。
AI-generated text typically displays less "burstiness" compared to human text, resulting in sentences of average length with conventional structures. This tendency sometimes makes AI-generated writing appear monotonous.
与人类文本相比，AI 生成的文本通常显示较少的“突发性”，从而导致具有传统结构的句子平均长度。这种趋势有时会使人工智能生成的写作显得单调。
Low burstiness suggests that a text is likely AI-generated.
低突发性表明文本可能是 AI 生成的。
A Potential Alternative: Watermarks 潜在的替代方案：水印
OpenAI, the organization behind ChatGPT, is actively exploring a "watermarking" system for AI-generated text. This system would involve embedding an invisible watermark into AI-generated content, allowing for its detection by another system to confirm its AI origin.
ChatGPT 背后的组织 OpenAI 正在积极探索用于 AI 生成文本的“水印”系统。该系统将涉及将不可见的水印嵌入到人工智能生成的内容中，允许另一个系统对其进行检测以确认其人工智能来源。
However, this watermarking system remains in development, with details on its functionality and effectiveness yet to be fully disclosed. It's also unclear whether these proposed watermarks will persist if the generated text undergoes editing. While this method shows promise for future AI detection, many uncertainties still surround its implementation.
然而，这种水印系统仍在开发中，其功能和有效性的细节尚未完全披露。目前还不清楚如果生成的文本经过编辑，这些提议的水印是否会持续存在。虽然这种方法为未来的人工智能检测带来了希望，但围绕其实施仍然存在许多不确定性。
How Reliable Are AI Detectors? AI 检测器的可靠性如何？
In practice, AI detectors often perform well, particularly with longer texts. However, they can falter when faced with AI output that has been deliberately made less predictable or when text has been edited or paraphrased after generation. Additionally, detectors can occasionally misidentify human-written text as AI-generated if it aligns with the criteria of low perplexity and burstiness.
在实践中，人工智能检测器通常表现良好，尤其是对于较长的文本。然而，当面对故意降低可预测性的 AI 输出时，或者当文本在生成后被编辑或释义时，他们可能会动摇。此外，如果符合低困惑度和突发性标准，检测器偶尔会将人工编写的文本误识别为 AI 生成的文本。
Our research into AI detectors indicates that no tool can guarantee complete accuracy. The highest accuracy we found was 84% in a premium tool or 68% in the best free tool. While these tools provide valuable insights into the likelihood of AI generation, it's crucial not to rely on them as sole evidence.
我们对 AI 探测器的研究表明，没有任何工具可以保证完全准确。我们发现，高级工具的最高准确率为84%，最佳免费工具的最高准确率为68%。虽然这些工具为人工智能生成的可能性提供了有价值的见解，但至关重要的是不要依赖它们作为唯一的证据。
As language models continue to evolve, detection tools will continually need to adapt to keep pace. Even the most confident providers acknowledge that their tools cannot serve as definitive evidence of AI generation. Universities and academic institutions, for the time being, maintain a cautious stance towards relying on these tools exclusively.
随着语言模型的不断发展，检测工具将需要不断调整以跟上步伐。即使是最有信心的供应商也承认，他们的工具不能作为人工智能生成的明确证据。目前，大学和学术机构对完全依赖这些工具持谨慎态度。
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