# Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

arXiv: https://arxiv.org/abs/2304.02868 · HTML: https://mlsanity.com/paper/2304.02868/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [AlphaStar: An Evolutionary Computation Perspective](https://mlsanity.com/paper/1902.01724/) (2019)
- [Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning](https://mlsanity.com/paper/2010.02386/) (2020)
- [NAIL: A General Interactive Fiction Agent](https://mlsanity.com/paper/1902.04259/) (2019)

## Built on by (most cited first, 30 of 30)

- [A Survey on Large Language Model-Based Game Agents](https://mlsanity.com/paper/2404.02039/) (2024)
- [LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models](https://mlsanity.com/paper/2404.01230/) (2024)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments](https://mlsanity.com/paper/2403.11807/) (2024)
- [LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay](https://mlsanity.com/paper/2310.14985/) (2023)
- [Entity Tracking in Language Models](https://mlsanity.com/paper/2305.02363/) (2023)
- [Do LLM Agents Have Regret? A Case Study in Online Learning and Games](https://mlsanity.com/paper/2403.16843/) (2024)
- [Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games](https://mlsanity.com/paper/2506.03610/) (2025)
- [G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning](https://mlsanity.com/paper/2505.13426/) (2025)
- [A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2403.10249/) (2024)
- [Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboard](https://mlsanity.com/paper/2407.07796/) (2024)
- [Are ChatGPT and GPT-4 Good Poker Players? -- A Pre-Flop Analysis](https://mlsanity.com/paper/2308.12466/) (2023)
- [Player-Driven Emergence in LLM-Driven Game Narrative](https://mlsanity.com/paper/2404.17027/) (2024)
- [Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering](https://mlsanity.com/paper/2411.11504/) (2024)
- [Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study](https://mlsanity.com/paper/2311.07387/) (2023)
- [SPRING: Studying the Paper and Reasoning to Play Games](https://mlsanity.com/paper/2305.15486/) (2023)
- [TALES: Text Adventure Learning Environment Suite](https://mlsanity.com/paper/2504.14128/) (2025)
- [How Well Can Vision Language Models See Image Details?](https://mlsanity.com/paper/2408.03940/) (2024)
- [Missed Connections: Lateral Thinking Puzzles for Large Language Models](https://mlsanity.com/paper/2404.11730/) (2024)
- [Agents Play Thousands of 3D Video Games](https://mlsanity.com/paper/2503.13356/) (2025)
- [Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models](https://mlsanity.com/paper/2305.10276/) (2023)
- [Exploring the Limits of ChatGPT in Software Security Applications](https://mlsanity.com/paper/2312.05275/) (2023)
- [Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making](https://mlsanity.com/paper/2506.12012/) (2025)
- [Take A Step Back: Rethinking the Two Stages in Visual Reasoning](https://mlsanity.com/paper/2407.19666/) (2024)
- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat](https://mlsanity.com/paper/2503.15726/) (2025)
- [Playing DOOM with 1.3M Parameters: Specialized Small Models vs Large Language Models for Real-Time Game Control](https://mlsanity.com/paper/2604.07385/) (2026)
- [A Text-to-Game Engine for UGC-Based Role-Playing Games](https://mlsanity.com/paper/2407.08195/) (2024)
- [Malinowski in the Age of AI: Can large language models create a text game based on an anthropological classic?](https://mlsanity.com/paper/2410.20536/) (2024)
- [High-quality generation of dynamic game content via small language models: A proof of concept](https://mlsanity.com/paper/2601.23206/) (2026)

## Abstract (arXiv)

Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users. In this technical report, we take an initiative to investigate their capacities of playing text games, in which a player has to understand the environment and respond to situations by having dialogues with the game world. Our experiments show that ChatGPT performs competitively compared to all the existing systems but still exhibits a low level of intelligence. Precisely, ChatGPT can not construct the world model by playing the game or even reading the game manual; it may fail to leverage the world knowledge that it already has; it cannot infer the goal of each step as the game progresses. Our results open up new research questions at the intersection of artificial intelligence, machine learning, and natural language processing.