# Evaluating Large Language Model Creativity from a Literary Perspective

arXiv: https://arxiv.org/abs/2312.03746 · HTML: https://mlsanity.com/paper/2312.03746/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [DeepCreativity: Measuring Creativity with Deep Learning Techniques](https://mlsanity.com/paper/2201.06118/) (2022)

## Built on by (most cited first, 7 of 7)

- [GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick](https://mlsanity.com/paper/2402.12948/) (2024)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [Analyzing Nobel Prize Literature with Large Language Models](https://mlsanity.com/paper/2410.18142/) (2024)
- [Existential Conversations with Large Language Models: Content, Community, and Culture](https://mlsanity.com/paper/2411.13223/) (2024)
- [INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges](https://mlsanity.com/paper/2401.05273/) (2024)
- [Creativity in AI as Emergence from Domain-Limited Generative Models](https://mlsanity.com/paper/2601.08388/) (2026)

## Abstract (arXiv)

This paper assesses the potential for large language models (LLMs) to serve as assistive tools in the creative writing process, by means of a single, in-depth case study. In the course of the study, we develop interactive and multi-voice prompting strategies that interleave background descriptions (scene setting, plot elements), instructions that guide composition, samples of text in the target style, and critical discussion of the given samples. We qualitatively evaluate the results from a literary critical perspective, as well as from the standpoint of computational creativity (a sub-field of artificial intelligence). Our findings lend support to the view that the sophistication of the results that can be achieved with an LLM mirrors the sophistication of the prompting.