STORYWARS: A Dataset and Instruction Tuning Baselines for Collaborative
  Story Understanding and Generation
Yulun Du Lydia Chilton
Collaborative stories, which are texts created through the collaborative
efforts of multiple authors with different writing styles and intentions, pose
unique challenges for NLP models. Understanding and generating such stories
remains an underexplored area due to the lack of open-domain corpora. To
address this, we introduce STORYWARS, a new dataset of over 40,000
collaborative stories written by 9,400 different authors from an online
platform. We design 12 task types, comprising 7 understanding and 5 generation
task types, on STORYWARS, deriving 101 diverse story-related tasks in total as
a multi-task benchmark covering all fully-supervised, few-shot, and zero-shot
scenarios. Furthermore, we present our instruction-tuned model, INSTRUCTSTORY,
for the story tasks showing that instruction tuning, in addition to achieving
superior results in zero-shot and few-shot scenarios, can also obtain the best
performance on the fully-supervised tasks in STORYWARS, establishing strong
multi-task benchmark performances on STORYWARS.