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Collaborative stories, which are texts created through the collaborativeefforts of multiple authors with different writing styles and intentions, poseunique challenges for NLP models. Understanding and generating such storiesremains an underexplored area due to the lack of open-domain corpora. Toaddress this, we introduce STORYWARS, a new dataset of over 40,000collaborative stories written by 9,400 different authors from an onlineplatform. We design 12 task types, comprising 7 understanding and 5 generationtask types, on STORYWARS, deriving 101 diverse story-related tasks in total asa multi-task benchmark covering all fully-supervised, few-shot, and zero-shotscenarios. Furthermore, we present our instruction-tuned model, INSTRUCTSTORY,for the story tasks showing that instruction tuning, in addition to achievingsuperior results in zero-shot and few-shot scenarios, can also obtain the bestperformance on the fully-supervised tasks in STORYWARS, establishing strongmulti-task benchmark performances on STORYWARS.

STORYWARS: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation