mlsanityData to 7 Oct 2026

Decoding the Black Box: Discerning AI Rhetorics About and Through Poetic Prompting

arXiv 2512.05243 ↗First posted 4 Dec 2025cs.CL cs.CYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 4 Dec 2025.

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4 of 18 references.

References · 4

  1. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  2. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 117
  3. 2022Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examplescited by 65
  4. 2024UnMASKed: Quantifying Gender Biases in Masked Language Models through Linguistically Informed Job Market Promptscited by 3

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