Decoding the Black Box: Discerning AI Rhetorics About and Through Poetic Prompting
This work offers a novel method for researchers and creatives to probe AI behavior, though it is incremental in the broader field of prompt engineering.
The study explores using poetic prompts to analyze large language models' tendencies and biases, finding that this approach can reveal how models adapt or rewrite creative works for different audiences.
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.