# Collaborative Storytelling and LLM: A Linguistic Analysis of Automatically-Generated Role-Playing Game Sessions

arXiv: https://arxiv.org/abs/2503.20623 · HTML: https://mlsanity.com/paper/2503.20623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023)
- [Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence](https://mlsanity.com/paper/2210.07109/) (2022)
- [Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning](https://mlsanity.com/paper/2105.01311/) (2021)
- [FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information](https://mlsanity.com/paper/2305.01528/) (2023)
- [Toward Automated Quest Generation in Text-Adventure Games](https://mlsanity.com/paper/1909.06283/) (2019)
- [Generating Interactive Worlds with Text](https://mlsanity.com/paper/1911.09194/) (2019)
- [Collaborative Storytelling with Human Actors and AI Narrators](https://mlsanity.com/paper/2109.14728/) (2021)
- [A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification](https://mlsanity.com/paper/2101.04017/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards a Linguistic Evaluation of Narratives: A Quantitative Stylistic Framework](https://mlsanity.com/paper/2604.19261/) (2026)

## Abstract (arXiv)

Role-playing games (RPG) are games in which players interact with one another to create narratives. The role of players in the RPG is largely based on the interaction between players and their characters. This emerging form of shared narrative, primarily oral, is receiving increasing attention. In particular, many authors investigated the use of an LLM as an actor in the game. In this paper, we aim to discover to what extent the language of Large Language Models (LLMs) exhibit oral or written features when asked to generate an RPG session without human interference. We will conduct a linguistic analysis of the lexical and syntactic features of the generated texts and compare the results with analyses of conversations, transcripts of human RPG sessions, and books. We found that LLMs exhibit a pattern that is distinct from all other text categories, including oral conversations, human RPG sessions and books. Our analysis has shown how training influences the way LLMs express themselves and provides important indications of the narrative capabilities of these tools.