论文标题
神经文本生成模型的逆向工程配置
Reverse Engineering Configurations of Neural Text Generation Models
论文作者
论文摘要
本文旨在对神经文本世代的基本特性有更深入的了解。对机器产生的文本出现的伪像的研究是一个新生的研究领域。以前，这些伪像在生成的文本中表面的程度和程度尚未得到很好的研究。 In the spirit of better understanding generative text models and their artifacts, we propose the new task of distinguishing which of several variants of a given model generated a piece of text, and we conduct an extensive suite of diagnostic tests to observe whether modeling choices (e.g., sampling methods, top-$k$ probabilities, model architectures, etc.) leave detectable artifacts in the text they generate.我们的主要发现得到了一套严格的实验，是存在此类工件，并且可以通过单独观察生成的文本来推断出不同的建模选择。这表明神经文本发生器可能比以前想象的要对各种建模选择更敏感。
This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models. The study of artifacts that emerge in machine generated text as a result of modeling choices is a nascent research area. Previously, the extent and degree to which these artifacts surface in generated text has not been well studied. In the spirit of better understanding generative text models and their artifacts, we propose the new task of distinguishing which of several variants of a given model generated a piece of text, and we conduct an extensive suite of diagnostic tests to observe whether modeling choices (e.g., sampling methods, top-$k$ probabilities, model architectures, etc.) leave detectable artifacts in the text they generate. Our key finding, which is backed by a rigorous set of experiments, is that such artifacts are present and that different modeling choices can be inferred by observing the generated text alone. This suggests that neural text generators may be more sensitive to various modeling choices than previously thought.
