用诗歌提示探查大模型的创作倾向与偏见。
Decoding the Black Box: Discerning AI Rhetorics About and Through Poetic Prompting
- 以诗歌模式作为提示，揭示大模型的生成偏好。
- 测试三款模型对著名诗人作品的改编意愿与效果。
- 适合研究模型创作逻辑或人机协作创作的读者。
提示工程已成为研究大型语言模型算法倾向与偏见的有效手段。同时，创作者与学者也利用大模型进行文本生成与编程，探索写作能力的边界。本研究提出，创意文本提示（特别是诗歌提示模式）可作为提示工程师的有力工具，并阐明其应用流程。随后，论文使用诗歌提示评估三款模型对一位著名诗人作品的描述与评价，并检验模型在迎合预期受众时，是否愿意修改或重写原始创造性作品的倾向及其后果。
原文摘要 · Abstract (English)
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.
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