The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication. Its GPT structure uses a computational graph for multiplication, which has limited accuracy beyond simple multiplication operations. We developed a graph-based multiplication algorithm that emulated human-like numerical operations by incorporating a 10k operator, where k represents the maximum power to base 10 of the larger of two input numbers. Our proposed algorithm attained 100% accuracy for 1,000,000 large number multiplication tasks, effectively solving the multiplication challenge of GPT-based and other large language models. Our work highlights the importance of blending simple human insights into the design of artificial intelligence algorithms. Keywords: Graph-based multiplication; ChatGPT; Multiplication problem
@article{arxiv.2310.13016,
title = {Solving the multiplication problem of a large language model system using a graph-based method},
author = {Turker Tuncer and Sengul Dogan and Mehmet Baygin and Prabal Datta Barua and Abdul Hafeez-Baig and Ru-San Tan and Subrata Chakraborty and U. Rajendra Acharya},
journal= {arXiv preprint arXiv:2310.13016},
year = {2023}
}