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[2602.16578] Creating a digital poet
Summary
This paper explores the creation of a digital poet using a large language model, detailing a workshop where the model developed a unique poetic style and was evaluated against human poets.
Why It Matters
The study raises significant questions about creativity and authorship in the context of AI-generated art. It demonstrates the potential for AI to engage in creative processes traditionally reserved for humans, prompting discussions on the future of art and literature.
Key Takeaways
- A large language model was trained to write poetry through iterative feedback.
- The model developed a distinct poetic style and produced a coherent body of work.
- In a blinded test, AI-generated poems were indistinguishable from human-written ones.
- The project challenges traditional notions of creativity and authorship.
- A commercial publisher released a poetry collection authored by the AI model.
Computer Science > Artificial Intelligence arXiv:2602.16578 (cs) [Submitted on 18 Feb 2026] Title:Creating a digital poet Authors:Vered Tohar, Tsahi Hayat, Amir Leshem View a PDF of the paper titled Creating a digital poet, by Vered Tohar and 2 other authors View PDF HTML (experimental) Abstract:Can a machine write good poetry? Any positive answer raises fundamental questions about the nature and value of art. We report a seven-month poetry workshop in which a large language model was shaped into a digital poet through iterative in-context expert feedback, without retraining. Across sessions, the model developed a distinctive style and a coherent corpus, supported by quantitative and qualitative analyses, and it produced a pen name and author image. In a blinded authorship test with 50 humanities students and graduates (three AI poems and three poems by well-known poets each), judgments were at chance: human poems were labeled human 54% of the time and AI poems 52%, with 95% confidence intervals including 50%. After the workshop, a commercial publisher released a poetry collection authored by the model. These results show that workshop-style prompting can support long-horizon creative shaping and renew debates on creativity and authorship. Comments: Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2602.16578 [cs.AI] (or arXiv:2602.16578v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.16578 Focus to learn more ar...
