使用GPT-4测量与修改英文文本的可读性
Measuring and Modifying the Readability of English Texts with GPT-4
AI总结：
本文实证研究GPT-4测量和修改英文文本可读性的能力，发现其零样本可读性估计与人类判断高度相关且优于传统方法，并能按需调整文本难度。
摘要：
大语言模型（LLMs）在其他领域的成功引发了一个问题：LLMs能否可靠地评估和操控文本的可读性。我们以实证方式探讨这一问题。首先，使用一个包含4,724段英文文本摘录的已发表语料库，我们发现由GPT-4 Turbo和GPT-4o mini以“零样本”方式产生的可读性估计与人类判断具有相对较高的相关性（分别为r = 0.76和r = 0.74），优于源自传统可读性公式和各种心理语言学指标的估计。然后，在一项预先注册的人类实验（N = 59）中，我们考察Turbo能否可靠地使文本变得更容易或更难阅读。我们发现了支持这一假设的证据，尽管人类判断中仍有相当大的方差未被解释。最后，我们讨论了该方法的局限性，包括有限的适用范围，以及“可读性”构念的效度及其对语境、受众和目标的依赖性。
英文摘要：
The success of Large Language Models (LLMs) in other domains has raised the question of whether LLMs can reliably assess and manipulate the readability of text. We approach this question empirically. First, using a published corpus of 4,724 English text excerpts, we find that readability estimates produced ``zero-shot'' from GPT-4 Turbo and GPT-4o mini exhibit relatively high correlation with human judgments (r = 0.76 and r = 0.74, respectively), out-performing estimates derived from traditional readability formulas and various psycholinguistic indices. Then, in a pre-registered human experiment (N = 59), we ask whether Turbo can reliably make text easier or harder to read. We find evidence to support this hypothesis, though considerable variance in human judgments remains unexplained. We conclude by discussing the limitations of this approach, including limited scope, as well as the validity of the ``readability'' construct and its dependence on context, audience, and goal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
