  Script learning studies how stereotypical events unfold, enabling machines to
reason about narratives with implicit information. Previous works mostly
consider a script as a linear sequence of events while ignoring the potential
branches that arise due to people's circumstantial choices. We hence propose
Choice-75, the first benchmark that challenges intelligent systems to make
decisions given descriptive scenarios, containing 75 scripts and more than 600
scenarios. We also present preliminary results with current large language
models (LLM). Although they demonstrate overall decent performance, there is
still notable headroom in hard scenarios.
