# Generating Interactive Worlds with Text

arXiv: https://arxiv.org/abs/1911.09194 · HTML: https://mlsanity.com/paper/1911.09194/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models](https://mlsanity.com/paper/2311.18232/) (2023)
- [Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models](https://mlsanity.com/paper/2304.13835/) (2023)
- [Can Language Models Serve as Text-Based World Simulators?](https://mlsanity.com/paper/2406.06485/) (2024)
- [Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence](https://mlsanity.com/paper/2210.07109/) (2022)
- [ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games](https://mlsanity.com/paper/2305.14879/) (2023)
- [Word2World: Generating Stories and Worlds through Large Language Models](https://mlsanity.com/paper/2405.06686/) (2024)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [Modeling Worlds in Text](https://mlsanity.com/paper/2106.09578/) (2021)
- [Situated Dialogue Learning through Procedural Environment Generation](https://mlsanity.com/paper/2110.03262/) (2021)
- [Ontologically Faithful Generation of Non-Player Character Dialogues](https://mlsanity.com/paper/2212.10618/) (2022)
- [Word2Minecraft: Generating 3D Game Levels through Large Language Models](https://mlsanity.com/paper/2503.16536/) (2025)
- [Collaborative Storytelling and LLM: A Linguistic Analysis of Automatically-Generated Role-Playing Game Sessions](https://mlsanity.com/paper/2503.20623/) (2025)
- [GenQuest: An LLM-based Text Adventure Game for Language Learners](https://mlsanity.com/paper/2510.04498/) (2025)

## Abstract (arXiv)

Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT. We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.