Generating Interactive Worlds with Text
Table 3: Comparison of Various Approaches to Worldbuilding. We report Hits at 1 on the test set for arranging locations and populating with objects, characters, and placing objects within container objects. Starspace models perform well on all tasks.
['[BOLD] Feature', '[BOLD] Model', '[BOLD] Locations', '[BOLD] Characters', '[BOLD] Objects', '[BOLD] Containers']
Leveraging the data distribution to weight random sampling provides a strong baseline for characters and objects, as a few are quite common. Providing the description text is helpful for improving prediction quality compared to having access only to the name feature. Amongst the various approaches, Starspace models show strong performance, particularly on the location prediction task. The Bi and Cross Encoder models are very large neural networks and may be overfitting on the much smaller LIGHT world creation training data. Further, they are pretrained on non-domain specific data, which may negatively impact performance.