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MathGAP: An Evaluation Benchmark for LLMs’ Mathematical Reasoning Using Controlled Proof Depth, Width, and Complexity for Out-of-Distribution Tasks
MathGAP is a new benchmark designed to evaluate the mathematical reasoning abilities of large language models (LLMs).
It focuses on challenging LLMs with complex mathematical problems that they haven't encountered before, using controlled parameters like proof depth and complexity to measure their performance.
This benchmark helps researchers understand the strengths and weaknesses of LLMs in mathematical problem-solving, providing valuable insights for future development.
MathGAP joins a growing collection of benchmarks specifically designed to evaluate various aspects of LLM performance, including tool usage, scientific research, code reasoning, rare disease understanding, and complex reasoning verification.
These benchmarks collectively contribute to a more complete understanding of LLMs' capabilities and limitations across different domains.
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By Michael IversenMathGAP is a new benchmark designed to evaluate the mathematical reasoning abilities of large language models (LLMs).
It focuses on challenging LLMs with complex mathematical problems that they haven't encountered before, using controlled parameters like proof depth and complexity to measure their performance.
This benchmark helps researchers understand the strengths and weaknesses of LLMs in mathematical problem-solving, providing valuable insights for future development.
MathGAP joins a growing collection of benchmarks specifically designed to evaluate various aspects of LLM performance, including tool usage, scientific research, code reasoning, rare disease understanding, and complex reasoning verification.
These benchmarks collectively contribute to a more complete understanding of LLMs' capabilities and limitations across different domains.