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Computer Science > Other Computer Science

arXiv:2310.13016 (cs)
[Submitted on 18 Oct 2023]

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

Authors:Turker Tuncer, Sengul Dogan, Mehmet Baygin, Prabal Datta Barua, Abdul Hafeez-Baig, Ru-San Tan, Subrata Chakraborty, U. Rajendra Acharya
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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
Comments: 9 pages, 3 figures
Subjects: Other Computer Science (cs.OH); Artificial Intelligence (cs.AI)
Cite as: arXiv:2310.13016 [cs.OH]
  (or arXiv:2310.13016v1 [cs.OH] for this version)
  https://doi.org/10.48550/arXiv.2310.13016

Submission history

From: Sengul Dogan [view email]
[v1] Wed, 18 Oct 2023 08:02:00 UTC (342 KB)
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