Solving the multiplication problem of a large language model system using a graph-based method

• URL: http://arxiv.org/abs/2310.13016v1
• Date: Wed, 18 Oct 2023 08:02:00 GMT
• Title: Solving the multiplication problem of a large language model system using a graph-based method
• Authors: 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
• Abstract summary: ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems. We developed a graph-based multiplication algorithm that emulated human-like numerical operations. Our proposed algorithm attained 100% accuracy for 1,000,000 large number multiplication tasks.
• Score: 20.43440908151311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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