# Zero-shot Mathematical Problem Solving via Generative Pre-trained Transformers

### Federico Galatolo, Mario Cimino, Gigliola Vaglini

#### 2022

#### Abstract

Mathematics is an effective testbed for measuring the problem-solving ability of machine learning models. The current benchmark for deep learning-based solutions is grade school math problems: given a natural language description of a problem, the task is to analyse the problem, exploit heuristics generated from a very large set of solved examples, and then generate an answer. In this paper, a descendant of the third generation of Generative Pre-trained Transformer Networks (GPT-3) is used to develop a zero-shot learning approach, to solve this problem. The proposed approach shows that coding based problem-solving is more effective than the natural language reasoning based one. Specifically, the architectural solution is built upon OpenAI Codex, a descendant of GPT-3 for programming tasks, trained on public GitHub repositories, the world’s largest source code hosting service. Experimental results clearly show the potential of the approach: by exploiting the Python as programming language, proposed pipeline achieves the 18.63% solve rate against the 6.82% of GPT-3. Finally, by using a fine-tuned verifier, the correctness of the answer can be ranked at runtime, and then improved by generating a predefined number of trials. With this approach, for 10 trials and an ideal verifier, the proposed pipeline achieves 54.20% solve rate.

Download#### Paper Citation

#### in Harvard Style

Galatolo F., Cimino M. and Vaglini G. (2022). **Zero-shot Mathematical Problem Solving via Generative Pre-trained Transformers**. In *Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,* ISBN 978-989-758-569-2, pages 479-483. DOI: 10.5220/0011032400003179

#### in Bibtex Style

@conference{iceis22,

author={Federico Galatolo and Mario Cimino and Gigliola Vaglini},

title={Zero-shot Mathematical Problem Solving via Generative Pre-trained Transformers},

booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

year={2022},

pages={479-483},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0011032400003179},

isbn={978-989-758-569-2},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,

TI - Zero-shot Mathematical Problem Solving via Generative Pre-trained Transformers

SN - 978-989-758-569-2

AU - Galatolo F.

AU - Cimino M.

AU - Vaglini G.

PY - 2022

SP - 479

EP - 483

DO - 10.5220/0011032400003179