MATHEMATICAL REASONING USING LARGE LANGUAGE MODELS

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MATHEMATICAL REASONING USING LARGE LANGUAGE MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240296294 titled 'MATHEMATICAL REASONING USING LARGE LANGUAGE MODELS

The patent application describes techniques for an AI system with a Large Language Model (LLM) that improves accuracy and reliability in solving mathematical problems.

Initial query is transformed into a template query by replacing input values with variables.
Multiple prompts are sent to the LLM, each contextually related to the template query.
Multiple results are received from the LLM, each including an analytical expression to solve the mathematical problem.
Expressions are evaluated using a numerical evaluation tool with randomly sampled values for variables.
Consensus is achieved when evaluated expressions satisfy a consensus condition.
Original inputs are evaluated with the expressions after consensus is reached, and the solution is output.

1. 1. Potential Applications:

This technology can be applied in educational settings for automated math problem-solving, in research for complex mathematical analysis, and in various industries for optimizing processes that involve mathematical calculations.

1. 1. Problems Solved:

This technology addresses the challenges of accurately and reliably solving mathematical problems using AI systems, improving efficiency and reducing errors in the process.

1. 1. Benefits:

The benefits of this technology include increased accuracy in mathematical problem-solving, enhanced reliability of AI systems, and improved efficiency in finding solutions to complex mathematical equations.

1. 1. Commercial Applications:

This technology can be commercialized in educational software for students, in financial institutions for data analysis, and in engineering firms for complex calculations, leading to improved productivity and accuracy in various industries.

1. 1. Prior Art:

Prior art related to this technology may include research papers on AI systems for mathematical problem-solving, patents on numerical evaluation tools, and academic studies on consensus algorithms in machine learning.

1. 1. Frequently Updated Research:

Researchers are constantly exploring new methods to enhance the accuracy and efficiency of AI systems in solving mathematical problems, with ongoing developments in LLM technology and numerical evaluation tools.

1. 1. 1. Questions about AI Technology:

1. How does this technology improve the accuracy of solving mathematical problems with AI systems? 2. What are the potential applications of this technology beyond mathematical problem-solving?

Original Abstract Submitted

disclosed are techniques for an ai system with a large language mode (llm) with improved accuracy and reliability in solving mathematical problems. an initial query is transformed into a template query by replacing the original input values with variables. multiple prompts are sent to the llm, each being different from one another, and contextually related to the template query. multiple results are responsively received from the llm, each result including an analytical expression to solve the mathematical problem. each of the expressions is evaluated using a numerical evaluation tool with variables of the expression being assigned a common set of randomly sampled values. a consensus is achieved when the evaluated expressions satisfy a consensus condition, such as when all outputs match consistently over n experiments or trials. after the consensus condition is reached, the original inputs are evaluated with one or more of the expressions, and the solution is output.