Grade School Math Word Problem Benchmark Dataset

via “mathematical problem-solving benchmark”

12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.

Unique: This benchmark uniquely combines a large dataset of challenging competition problems with a robust evaluation framework for language models.

vs others: Unlike other benchmarks, MATH offers a comprehensive set of competition-level problems specifically designed for rigorous evaluation of mathematical reasoning in AI models.

via “grade-school science question benchmark evaluation”

7.8K science questions testing genuine reasoning, not just recall.

Unique: Explicitly designed to filter out questions answerable by retrieval or word co-occurrence — the Challenge subset (2,590 questions) was curated by removing questions that simple baseline methods could solve, ensuring the remaining questions require genuine multi-step reasoning and knowledge application rather than surface-level pattern matching

vs others: More rigorous than generic QA benchmarks because it explicitly excludes questions solvable by shallow methods, making it a stricter test of reasoning; smaller and more focused than MMLU but with deeper curation for reasoning-specific evaluation

via “benchmark dataset for mathematical reasoning”

12.5K competition math problems across 7 subjects and 5 difficulty levels.

Unique: This dataset includes detailed step-by-step solutions for each problem, making it unique for training AI in mathematical reasoning.

vs others: Unlike other datasets, MATH provides a structured approach to evaluating mathematical reasoning with competition-level problems and solutions.

via “linguistically diverse problem corpus with controlled reasoning complexity”

8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.

Unique: Human-authored problems with explicit step-count constraints (2-8 steps) and linguistic diversity ensure that models cannot solve problems through surface-level pattern matching or memorization, forcing evaluation of genuine multi-step reasoning capability

vs others: More challenging than synthetic or template-based benchmarks (human authorship prevents exploitable patterns) and more stable than crowdsourced datasets (controlled authorship ensures consistency), but smaller than web-scraped math problem collections

via “advanced mathematical problem evaluation”

Competition mathematics problems (harder than GSM8K)

Unique: MATH's dataset is specifically curated from high school math contests, providing a unique challenge that is more difficult than typical benchmarks, allowing for a clearer differentiation of model capabilities.

vs others: More challenging than GSM8K, making it a superior choice for evaluating advanced mathematical reasoning in AI models.

via “grade-school math word problem benchmark dataset”

Dataset by openai. 8,78,005 downloads.

Unique: Specifically designed for evaluating chain-of-thought reasoning in LLMs with explicit solution step annotations, rather than just problem-answer pairs. The dataset includes intermediate reasoning steps that enable fine-grained analysis of how models decompose multi-step arithmetic problems, making it architecturally distinct from simple QA datasets that only provide final answers.

vs others: More focused on reasoning process evaluation than MATH or AQuA datasets because it explicitly captures solution chains, enabling assessment of intermediate step quality rather than just final answer accuracy.