mlsanityData to 7 Oct 2026

Malinowski in the Age of AI: Can large language models create a text game based on an anthropological classic?

arXiv 2410.20536 ↗First posted 27 Oct 2024cs.HC cs.AI cs.CYcited by 1

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Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) like ChatGPT and GPT-4 have shown remarkable abilities in a wide range of tasks such as summarizing texts and assisting in coding. Scientific research has demonstrated that these models can also play text-adventure games. This study aims to explore whether LLMs can autonomously create text-based games based on anthropological classics, evaluating also their effectiveness in communicating knowledge. To achieve this, the study engaged anthropologists in discussions to gather their expectations and design inputs for an anthropologically themed game. Through iterative processes following the established HCI principle of 'design thinking', the prompts and the conceptual framework for crafting these games were refined. Leveraging GPT3.5, the study created three prototypes of games centered around the seminal anthropological work of the social anthropologist's Bronislaw Malinowski's "Argonauts of the Western Pacific" (1922). Subsequently, evaluations were conducted by inviting senior anthropologists to playtest these games, and based on their inputs, the game designs were refined. The tests revealed promising outcomes but also highlighted key challenges: the models encountered difficulties in providing in-depth thematic understandings, showed suspectibility to misinformation, tended towards monotonic responses after an extended period of play, and struggled to offer detailed biographical information. Despite these limitations, the study's findings open up new research avenues at the crossroads of artificial intelligence, machine learning, LLMs, ethnography, anthropology and human-computer interaction.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 44 references that are in the map. First posted on arXiv 27 Oct 2024.

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5 of 44 references.

References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  3. 2023Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questionscited by 30
  4. 2019NAIL: A General Interactive Fiction Agentcited by 17
  5. 2023Can ChatGPT Pass An Introductory Level Functional Language Programming Course?cited by 9

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