Playing With AI: How Do State-Of-The-Art Large Language Models Perform in the 1977 Text-Based Adventure Game Zork?
#Large Language Models #Zork #Text‑Based Adventure #Problem‑Solving #Reasoning #Dialogue #Artificial Intelligence #Evaluation #Natural Language Processing
📌 Key Takeaways
- The paper evaluates contemporary LLMs’ problem‑solving and reasoning in the 1977 text‑based adventure game Zork.
- Zork’s dialogue‑based gameplay provides a controlled setting for assessing natural‑language understanding and action planning.
- Researchers test how LLM chatbots interpret game descriptions and produce action sequences needed to progress.
- The study seeks to benchmark LLM performance against a complex, stateful narrative environment.
📖 Full Retelling
Researchers in February 2026 published a positioning paper on arXiv titled "Playing With AI: How Do State‑Of‑The‑Art Large Language Models Perform in the 1977 Text‑Based Adventure Game Zork?" The study examines how contemporary large language models (LLMs) interpret natural‑language descriptions and generate action sequences to succeed in the classic 1977 text‑adventure game Zork. By using the game’s dialogue‑heavy structure, the authors aim to benchmark the problem‑solving and reasoning abilities of LLM‑based chatbots in a controlled environment.
🏷️ Themes
Large Language Models, Game‑Based Evaluation, Natural Language Understanding, Problem‑Solving & Reasoning
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Original Source
arXiv:2602.15867v1 Announce Type: cross
Abstract: In this positioning paper, we evaluate the problem-solving and reasoning capabilities of contemporary Large Language Models (LLMs) through their performance in Zork, the seminal text-based adventure game first released in 1977. The game's dialogue-based structure provides a controlled environment for assessing how LLM-based chatbots interpret natural language descriptions and generate appropriate action sequences to succeed in the game. We test
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