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Operations in many essential industries including finance and banking areoften characterized by the need to perform repetitive sequential tasks. Despitetheir criticality to the business, workflows are rarely fully automated or evenformally specified, though there may exist a number of natural languagedocuments describing these procedures for the employees of the company. Planextraction methods provide us with the possibility of extracting structureplans from such natural language descriptions of the plans/workflows, whichcould then be leveraged by an automated system. In this paper, we investigatethe utility of generalized language models in performing such extractionsdirectly from such texts. Such models have already been shown to be quiteeffective in multiple translation tasks, and our initial results seem to pointto their effectiveness also in the context of plan extractions. Particularly,we show that GPT-3 is able to generate plan extraction results that arecomparable to many of the current state of the art plan extraction methods.

GPT3-to-plan: Extracting plans from text using GPT-3