# Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative Writing

arXiv: https://arxiv.org/abs/2107.04007 · HTML: https://mlsanity.com/paper/2107.04007/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Enabling Language Models to Fill in the Blanks](https://mlsanity.com/paper/2005.05339/) (2020)
- [Do Massively Pretrained Language Models Make Better Storytellers?](https://mlsanity.com/paper/1909.10705/) (2019)
- [MaskGAN: Better Text Generation via Filling in the______](https://mlsanity.com/paper/1801.07736/) (2018)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Blank Language Models](https://mlsanity.com/paper/2002.03079/) (2020)
- [Decoding Methods for Neural Narrative Generation](https://mlsanity.com/paper/2010.07375/) (2020)
- [Heteroglossia: In-Situ Story Ideation with the Crowd](https://mlsanity.com/paper/2001.04519/) (2020)
- [DeepTingle](https://mlsanity.com/paper/1705.03557/) (2017)
- [Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling](https://mlsanity.com/paper/1912.00698/) (2019)
- [Reading Between the Lines: Exploring Infilling in Visual Narratives](https://mlsanity.com/paper/2010.13944/) (2020)

## Built on by (most cited first, 14 of 14)

- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers](https://mlsanity.com/paper/2211.05030/) (2022)
- [Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking](https://mlsanity.com/paper/2410.03703/) (2024)
- [How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment](https://mlsanity.com/paper/2401.13481/) (2024)
- [Affective and Dynamic Beam Search for Story Generation](https://mlsanity.com/paper/2310.15079/) (2023)
- [Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship](https://mlsanity.com/paper/2208.00870/) (2022)
- [Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts](https://mlsanity.com/paper/2405.01660/) (2024)
- [Phraselette: A Poet's Procedural Palette](https://mlsanity.com/paper/2503.06335/) (2025)
- [The Case for a Single Model that can Both Generate Continuations and Fill in the Blank](https://mlsanity.com/paper/2206.04812/) (2022)
- [Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction](https://mlsanity.com/paper/2302.09122/) (2023)
- [Computational Storytelling and Emotions: A Survey](https://mlsanity.com/paper/2205.10967/) (2022)
- [Controllable Response Generation for Assistive Use-cases](https://mlsanity.com/paper/2112.02246/) (2021)
- [Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking](https://mlsanity.com/paper/2411.00168/) (2024)
- [COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes](https://mlsanity.com/paper/2510.14763/) (2025)

## Abstract (arXiv)

Getting machines to generate text perceived as creative is a long-pursued goal. A growing body of research directs this goal towards augmenting the creative writing abilities of human authors. In this paper, we pursue this objective by analyzing how observing examples of automatically generated text influences writing. In particular, we examine a task referred to as sentence infilling, which involves transforming a list of words into a complete sentence. We emphasize "storiability" as a desirable feature of the resulting sentences, where "storiable" sentences are those that suggest a story a reader would be curious to hear about. Both humans and an automated system (based on a neural language model) performed this sentence infilling task. In one setting, people wrote sentences on their own; in a different setting, people observed the sentences produced by the model while writing their own sentences. Readers then assigned storiability preferences to the resulting sentences in a subsequent evaluation. We find that human-authored sentences were judged as more storiable when authors observed the generated examples, and that storiability increased as authors derived more semantic content from the examples. This result gives evidence of an "inspiration through observation" paradigm for human-computer collaborative writing, through which human writing can be enhanced by text generation models without directly copying their output.