论文标题
不是：使用GPT的反事实新闻
The COVID That Wasn't: Counterfactual Journalism Using GPT
论文作者
论文摘要
在本文中，我们探讨了使用大语言模型来评估人类对现实世界事件的解释的使用。为此，鉴于大流行期间写的实际文章的头条新闻，我们使用在2020年之前接受过培训的语言模型来人为地生成有关Covid-19的新闻文章。然后，我们将人工产生的语料库的风格素质与新闻语料库进行比较，在这种情况下，由CBC新闻在2020年1月23日至5月5日之间发表的5,082篇文章。我们发现，我们的人为生成的文章对Covid表现出更大的负面态度，并且对COVID的态度更大，并且对地球构架的依赖性较低。对于寻求通过最新文本生成的突破来模拟大规模文化过程的研究人员来说，我们的方法和结果非常重要。
In this paper, we explore the use of large language models to assess human interpretations of real world events. To do so, we use a language model trained prior to 2020 to artificially generate news articles concerning COVID-19 given the headlines of actual articles written during the pandemic. We then compare stylistic qualities of our artificially generated corpus with a news corpus, in this case 5,082 articles produced by CBC News between January 23 and May 5, 2020. We find our artificially generated articles exhibits a considerably more negative attitude towards COVID and a significantly lower reliance on geopolitical framing. Our methods and results hold importance for researchers seeking to simulate large scale cultural processes via recent breakthroughs in text generation.
