论文标题
检测大规模语言模型产生的文本的限制
Limits of Detecting Text Generated by Large-Scale Language Models
论文作者
论文摘要
有些人将大规模的语言模型视为可以产生长而连贯的文本的危险模型，因为它们可能用于错误信息广告系列。在这里，我们将大规模的语言模型输出检测作为假设测试问题，以将文本归类为真实或生成。我们表明，特定语言模型的错误指数是根据其困惑性的限制的，这是语言生成性能的标准衡量标准。在假设人类语言是静止和崇高的假设下，该公式从考虑特定的语言模型到考虑最大似然语言模型，在K级马尔可夫近似等级中。误差概率已表征。还进行了一些有关合并语义侧信息的讨论。
Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated. We show that error exponents for particular language models are bounded in terms of their perplexity, a standard measure of language generation performance. Under the assumption that human language is stationary and ergodic, the formulation is extended from considering specific language models to considering maximum likelihood language models, among the class of k-order Markov approximations; error probabilities are characterized. Some discussion of incorporating semantic side information is also given.
