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

TL;DR

This paper explores using GPT-3 to extract structured plans from natural language descriptions, demonstrating its effectiveness comparable to existing specialized methods in automating workflows.

Contribution

It introduces a novel application of GPT-3 for plan extraction from text, showing its potential as a versatile tool in workflow automation.

Findings

GPT-3 achieves comparable results to state-of-the-art plan extraction methods.

Natural language processing with GPT-3 can facilitate automation in industries.

The approach simplifies plan extraction without extensive domain-specific training.

Abstract

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.