[PDF] GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond | Semantic Scholar

- MathPrompter improves performance of LLMs on arithmetic problems. - It uses Zero-shot chain-of-thought prompting technique. - Generates multiple algebraic expressions or python functions to solve math problems. - Raises confidence level in the output results.

– Paper proposes ‘MathPrompter’ technique to improve LLMs performance on arithmetic problems.
– Uses Zero-shot chain-of-thought prompting to generate multiple solutions.
– Aims to increase confidence level in the output results.

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– Improved performance of LLMs on arithmetic problems
– Increased reliance on predictions
– Confidence in output results
– Multiple ways to solve math problems using algebraic expressions or python functions

– MathPrompter improves performance of LLMs on arithmetic problems.
– It uses Zero-shot chain-of-thought prompting technique to generate multiple solutions.
– This raises confidence level in the output results.

– MathPrompter improves performance of LLMs on arithmetic problems.
– MathPrompter generates multiple algebraic expressions or python functions to solve math problems.
– MathPrompter increases confidence level in the output results.

– MathPrompter improves performance of LLMs on arithmetic problems.
– MathPrompter generates multiple algebraic expressions or python functions to solve math problems.
– MathPrompter increases confidence level in the output results.

– MathPrompter is a technique that helps computers solve math problems.
– It uses different ways to solve the same problem and increases confidence in the results.