  Operations in many essential industries including finance and banking are
often characterized by the need to perform repetitive sequential tasks. Despite
their criticality to the business, workflows are rarely fully automated or even
formally specified, though there may exist a number of natural language
documents describing these procedures for the employees of the company. Plan
extraction methods provide us with the possibility of extracting structure
plans from such natural language descriptions of the plans/workflows, which
could then be leveraged by an automated system. In this paper, we investigate
the utility of generalized language models in performing such extractions
directly from such texts. Such models have already been shown to be quite
effective in multiple translation tasks, and our initial results seem to point
to their effectiveness also in the context of plan extractions. Particularly,
we show that GPT-3 is able to generate plan extraction results that are
comparable to many of the current state of the art plan extraction methods.
