解码黑箱：通过诗意提示 discerning AI 的修辞
Decoding the Black Box: Discerning AI Rhetorics About and Through Poetic Prompting
AI总结：
通过诗意提示评估AI模型对诗人作品的描述和评价，探讨模型在受众面前适应和重写创意作品的能力。
摘要：
提示工程已成为研究大型语言模型算法倾向和偏见的一种有用方法。同时，创作者和学者利用LLMs开发创意作品，并通过文本生成和代码探索其写作能力的边界。本研究建议，创意文本提示，特别是诗歌提示模式，可能成为提示工程师工具箱中的有用补充，并概述了这一方法可能被采用的过程。然后，论文使用诗意提示来评估三位著名诗人模型的描述和评价，并测试模型在假定受众面前适应或重写原始创意作品的后果。
英文摘要：
Prompt engineering has emerged as a useful way studying the algorithmic tendencies and biases of large language models. Meanwhile creatives and academics have leveraged LLMs to develop creative works and explore the boundaries of their writing capabilities through text generation and code. This study suggests that creative text prompting, specifically Poetry Prompt Patterns, may be a useful addition to the toolbox of the prompt engineer, and outlines the process by which this approach may be taken. Then, the paper uses poetic prompts to assess descriptions and evaluations of three models of a renowned poet and test the consequences of the willingness of models to adapt or rewrite original creative works for presumed audiences.
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。
