Collaborative Storytelling with Large-scale Neural Language Models
Open accessAbstract
Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far. We constructed the storytelling system by tuning a publicly-available large scale language model on a dataset of writing prompts and their accompanying fictional works. We identify generating sufficiently human-like utterances to be an important technical issue and propose a sample-and-rank approach to improve utterance quality. Quantitative evaluation shows that our approach outperforms a baseline, and we present qualitative evaluation of our system’s capabilities.
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In the Formal context, reviewing support was limited and focused on evaluation using quantitative feedback like readability metrics and visual feedback such as progress bars.
“Finally, reviewing support was limited, and focused on evaluation using quantitative feedback like readability metrics [88], and visual feedback such as progress bars [90].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [90]
Citation [90] is attached to the claim about visual feedback such as progress bars, not to the readability-metrics example attributed to [88]. The paper’s evaluation discussion and Figure 5 describe stacked bar graphs showing evaluators’ preferences between systems, but do not describe progress bars or visual feedback for reviewing a story (searched: progress bars, reviewing support, feedback). (LLM, conf=0.76)
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Cited by
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Training Verifiers to Solve Math Word Problems
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- Authors
- Nichols, E., Gao, L., & Gomez, R.
- Affiliations
- Honda (Japan) (JP)
- OrientExpress Technologies Inc.
- Honda Research Institute Japan
- ORCID
- Nichols, Eric
- Venue
- Motion, Interaction and Games
- Publisher
- ACM
- Published
- 2020-10-16
- Pages
- 1-10
- OpenAlex
- W3108950497
- Cited by
- 50
- References
- 4
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 17 · 2023: 5 · 2022: 5 · 2021: 6
- FWCI
- 1.53
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- creativecommons.org/licenses/by-nc-sa/4.0/ (vor)
- Locations
- Motion in Games (publishedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- collaborative storytelling, human-AI collaboration, large language models, turn-taking, story generation, prompt tuning, fictional works
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