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

arXiv: https://arxiv.org/abs/2106.07131 · HTML: https://mlsanity.com/paper/2106.07131/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences](https://mlsanity.com/paper/1506.04089/) (2015)
- [Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation](https://mlsanity.com/paper/1610.03164/) (2016)
- [Extracting Action Sequences from Texts Based on Deep Reinforcement Learning](https://mlsanity.com/paper/1803.02632/) (2018)

## Built on by (most cited first, 26 of 26)

- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](https://mlsanity.com/paper/2402.01817/) (2024)
- [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023)
- [LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench](https://mlsanity.com/paper/2409.13373/) (2024)
- [Distilling Script Knowledge from Large Language Models for Constrained Language Planning](https://mlsanity.com/paper/2305.05252/) (2023)
- [Plansformer: Generating Symbolic Plans using Transformers](https://mlsanity.com/paper/2212.08681/) (2022)
- [PREGO: online mistake detection in PRocedural EGOcentric videos](https://mlsanity.com/paper/2404.01933/) (2024)
- [Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis](https://mlsanity.com/paper/2310.10477/) (2023)
- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation](https://mlsanity.com/paper/2308.08784/) (2023)
- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://mlsanity.com/paper/2202.02886/) (2022)
- [Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1](https://mlsanity.com/paper/2410.02162/) (2024)
- [Exploiting Language Models as a Source of Knowledge for Cognitive Agents](https://mlsanity.com/paper/2310.06846/) (2023)
- [Towards Healthy AI: Large Language Models Need Therapists Too](https://mlsanity.com/paper/2304.00416/) (2023)
- [Transformers in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.05979/) (2023)
- [TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos](https://mlsanity.com/paper/2411.02570/) (2024)
- [a survey on GPT-3](https://mlsanity.com/paper/2212.00857/) (2022)
- [AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events](https://mlsanity.com/paper/2309.16150/) (2023)
- [Improved Instruction Ordering in Recipe-Grounded Conversation](https://mlsanity.com/paper/2305.17280/) (2023)
- [Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks](https://mlsanity.com/paper/2208.09554/) (2022)
- [Improving Language Model Prompting in Support of Semi-autonomous Task Learning](https://mlsanity.com/paper/2209.07636/) (2022)
- [Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge](https://mlsanity.com/paper/2202.07138/) (2022)
- [Automated Action Model Acquisition from Narrative Texts](https://mlsanity.com/paper/2307.10247/) (2023)
- [Enhancing Mobile "How-to" Queries with Automated Search Results Verification and Reranking](https://mlsanity.com/paper/2404.08860/) (2024)
- [Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis](https://mlsanity.com/paper/2306.06770/) (2023)

## Abstract (arXiv)

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.