Malinowski in the Age of AI: Can large language models create a text game based on an anthropological classic?
October 27, 2024 Β· Declared Dead Β· π Proceedings of the 21th International Conference on Culture and Computer Science: from Humanism to Digital Humanities
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Authors
Michael Peter Hoffmann, Jan Fillies, Adrian Paschke
arXiv ID
2410.20536
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CY
Citations
2
Venue
Proceedings of the 21th International Conference on Culture and Computer Science: from Humanism to Digital Humanities
Last Checked
4 months ago
Abstract
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.
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