In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' reactions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts readers' decisions, focusing on both amateur and expert audiences. Our findings indicate that GPT-4 can generate persuasive analyses affecting the decisions of both amateurs and professionals. Furthermore, we evaluate the generated text from the aspects of grammar, convincingness, logical coherence, and usefulness. The results highlight a high correlation between real-world evaluation through audience reactions and the current multi-dimensional evaluators commonly used for generative models. Overall, this paper shows the potential and risk of using generated text to sway human decisions and also points out a new direction for evaluating generated text, i.e., leveraging the reactions and decisions of readers. We release our dataset to assist future research.

本研究探讨了大型语言模型（LLMs）生成文本如何影响读者决策，填补了评估生成文本的新空白。论文指出，GPT-4能够生成具有说服力的分析，影响业余和专业读者的决策，显示生成文本在决策过程中的潜在影响与风险，并提出了一种基于读者反应的新评估方法。 

超越图灵测试：GPT-4能否影响专家决策？