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Various human activities can be abstracted into a sequence of actions innatural text, i.e. cooking, repairing, manufacturing, etc. Such actionsequences heavily depend on the executing order, while disorder in actionsequences leads to failure of further task execution by robots or AI agents.Therefore, to verify the order reasoning capability of current neural models insequential tasks, we propose a challenging benchmark , named STEPS. STEPSinvolves two subtask settings, focusing on determining the rationality of givennext step in recipes and selecting the reasonable step from the multi-choicequestion, respectively. We describe the data construction and taskformulations, and benchmark most of significant Large Language Models (LLMs).The experimental results demonstrate 1) The commonsense reasoning of actionorders in sequential tasks are challenging to resolve via zero-shot promptingor few-shot in-context learning for LLMs; 2) Prompting method stillsignificantly lags behind tuning-based method on STEPS.

STEPS: A Benchmark for Order Reasoning in Sequential Tasks