GPT3-to-plan: Extracting plans from text using GPT-3
This addresses the need for automating repetitive sequential tasks in essential industries, though it is incremental as it applies an existing model to a new domain.
The paper tackles the problem of extracting structured plans from natural language descriptions of workflows in industries like finance, using GPT-3 to generate results comparable to current state-of-the-art plan extraction methods.
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.