2023

STEPS: A Benchmark for Order Reasoning in Sequential Tasks

Wang, Weizhi, Wang, Hong, Yan, Xifeng

Understand

Various human activities can be abstracted into a sequence of actions in natural text, i.e.

  • cooking, repairing, manufacturing, etc.
  • Such action sequences heavily depend on the executing order, while disorder in action sequences leads to failure of further task execution by robots or AI agents.
  • Therefore, to verify the order reasoning capability of current neural models in sequential tasks, we propose a challenging benchmark , named STEPS.

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