# Modeling Worlds in Text

arXiv: https://arxiv.org/abs/2106.09578 · HTML: https://mlsanity.com/paper/2106.09578/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Keep CALM and Explore: Language Models for Action Generation in Text-based Games](https://mlsanity.com/paper/2010.02903/) (2020)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [Event Representations for Automated Story Generation with Deep Neural Nets](https://mlsanity.com/paper/1706.01331/) (2017)
- [Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1812.01628/) (2018)
- [Learning Dynamic Belief Graphs to Generalize on Text-Based Games](https://mlsanity.com/paper/2002.09127/) (2020)
- [Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://mlsanity.com/paper/1910.08435/) (2019)
- [How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds](https://mlsanity.com/paper/2006.07409/) (2020)
- [Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines](https://mlsanity.com/paper/2010.03790/) (2020)
- [Bringing Stories Alive: Generating Interactive Fiction Worlds](https://mlsanity.com/paper/2001.10161/) (2020)
- [Playing Text-Based Games with Common Sense](https://mlsanity.com/paper/2012.02757/) (2020)
- [Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge](https://mlsanity.com/paper/2005.00811/) (2020)
- [Process-Level Representation of Scientific Protocols with Interactive Annotation](https://mlsanity.com/paper/2101.10244/) (2021)
- [Generating Interactive Worlds with Text](https://mlsanity.com/paper/1911.09194/) (2019)
- [Neural Game Engine: Accurate learning of generalizable forward models from pixels](https://mlsanity.com/paper/2003.10520/) (2020)

## Built on by (most cited first, 11 of 11)

- [Latent Diffusion Energy-Based Model for Interpretable Text Modeling](https://mlsanity.com/paper/2206.05895/) (2022)
- [Aligning to Social Norms and Values in Interactive Narratives](https://mlsanity.com/paper/2205.01975/) (2022)
- [Making Large Language Models into World Models with Precondition and Effect Knowledge](https://mlsanity.com/paper/2409.12278/) (2024)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training](https://mlsanity.com/paper/2502.06589/) (2025)
- [A Minimal Approach for Natural Language Action Space in Text-based Games](https://mlsanity.com/paper/2305.04082/) (2023)
- [Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game](https://mlsanity.com/paper/2406.11012/) (2024)
- [Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games](https://mlsanity.com/paper/2304.07258/) (2023)
- [Dual-Scale World Models for LLM Agents Towards Hard-Exploration Problems](https://mlsanity.com/paper/2509.24116/) (2025)
- [Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games](https://mlsanity.com/paper/2311.01928/) (2023)
- [Infusing Commonsense World Models with Graph Knowledge](https://mlsanity.com/paper/2301.05746/) (2023)

## Abstract (arXiv)

We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds. Our dataset provides 24198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7836 heldout instances over 9 additional games in the test set. We further provide baseline models using rules-based, question-answering, and sequence learning approaches in addition to an analysis of the data and corresponding learning tasks.