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

By Alberto Olmo, Sarath Sreedharan, Subbarao Kambhampati. 2021

49 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Large Language Models, LLM Agents & Tool Use

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sarath Sreedharan
2. Subbarao Kambhampati
3. Lin Guan
4. Tathagata Chakraborti

In LLM Agents & Tool Use

1. Subbarao Kambhampati
2. Sarath Sreedharan

Citations in the corpus

• Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning (2023, background): “Recent studies have shown the efficacy of LLMs in translating natural language to formal descriptions [35] or constructing PDDL goals from natural-language instructions [58, 32].”