Solving the multiplication problem of a large language model system using a graph-based method
This solves a specific arithmetic limitation in large language models like ChatGPT, which is incremental as it addresses a known bottleneck without broader AI implications.
The authors tackled the multiplication accuracy problem in GPT-based chatbots by developing a graph-based algorithm that emulates human-like numerical operations, achieving 100% accuracy on 1,000,000 large number multiplication tasks.
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