gsm8k
DatasetFreeDataset by openai. 8,22,680 downloads.
Capabilities5 decomposed
grade-school math word problem benchmark dataset
Medium confidenceProvides 8,522 crowdsourced grade-school math word problems with step-by-step solutions and final numerical answers. The dataset is structured as parquet files containing problem text, solution chains, and answer labels, enabling evaluation of language models' mathematical reasoning and arithmetic capabilities through standardized benchmarking. Problems range from single-step to multi-step arithmetic requiring intermediate reasoning steps.
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.
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.
multi-format dataset loading and serialization
Medium confidenceSupports loading and exporting the benchmark dataset through multiple data processing libraries (pandas, polars, MLCroissant) and formats (parquet, JSON), enabling seamless integration into diverse ML pipelines and analysis workflows. The dataset is registered with HuggingFace's datasets library, providing automatic caching, versioning, and streaming capabilities without manual file management.
Integrates with HuggingFace's datasets library ecosystem, providing automatic versioning, caching, and streaming without manual file management. Unlike raw parquet files, the dataset includes metadata registration enabling one-line loading with `datasets.load_dataset('openai/gsm8k')` and automatic handling of train/test splits.
More convenient than manually downloading and parsing parquet files because it provides automatic caching, version management, and split handling through the datasets library, reducing boilerplate code in evaluation scripts.
train-test split evaluation framework
Medium confidenceProvides pre-defined train and test splits enabling standardized evaluation protocols where models are trained on the training subset and evaluated on held-out test data. The split structure is built into the dataset metadata, ensuring reproducibility across different research teams and preventing data leakage through automatic enforcement of partition boundaries.
Provides official, immutable train-test splits managed through HuggingFace's dataset versioning system, ensuring all published results reference identical test sets. This architectural choice enables direct comparison across papers and prevents accidental benchmark contamination through automatic partition enforcement.
More reproducible than custom train-test splits because the official splits are version-controlled and immutable, preventing the drift and inconsistency that occurs when different teams create their own partitions from the same raw data.
crowdsourced problem-solution annotation pipeline
Medium confidenceContains 8,522 math problems with step-by-step solutions created through crowdsourced annotation, where human annotators generated both problem statements and solution chains. The annotation structure captures intermediate reasoning steps, enabling evaluation of models' ability to produce human-like solution processes rather than just final answers. Quality control mechanisms are embedded in the crowdsourcing workflow to maintain consistency.
Explicitly captures solution chains with intermediate reasoning steps rather than just problem-answer pairs, enabling training and evaluation of models' reasoning process quality. The crowdsourced annotation approach ensures solutions reflect human problem-solving patterns, making it suitable for training models to produce human-like explanations.
More suitable for reasoning-focused training than synthetic or automatically-generated datasets because human annotators naturally produce step-by-step solutions that reflect realistic problem decomposition strategies, rather than optimized-for-parsing formats.
standardized benchmark evaluation protocol
Medium confidenceServes as an official benchmark dataset registered in the ML community (822,680 downloads on HuggingFace), enabling standardized comparison of model reasoning capabilities across published research. The dataset includes metadata (arxiv reference, MIT license) establishing it as a canonical evaluation resource, with built-in versioning ensuring reproducibility across time and model iterations.
Established as an official benchmark through academic publication (arxiv:2110.14168) and high adoption (822,680 downloads), creating network effects where publishing results on GSM8K becomes standard practice. The dataset includes evaluation YAML specifications enabling automated benchmark execution and result comparison.
More authoritative than custom evaluation datasets because it has academic publication backing, widespread adoption in published papers, and built-in evaluation specifications, making it the de facto standard for reasoning benchmarking rather than one of many competing datasets.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with gsm8k, ranked by overlap. Discovered automatically through the match graph.
GSM8K
8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.
MATH Benchmark
12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.
ai2_arc
Dataset by allenai. 4,06,798 downloads.
CodeContests
13K competitive programming problems from AlphaCode research.
promptbench
PromptBench is a powerful tool designed to scrutinize and analyze the interaction of large language models with various prompts. It provides a convenient infrastructure to simulate **black-box** adversarial **prompt attacks** on the models and evaluate their performances.
PromptBench
Microsoft's unified LLM evaluation and prompt robustness benchmark.
Best For
- ✓ML researchers evaluating reasoning capabilities of large language models
- ✓teams building math tutoring or educational AI systems
- ✓developers implementing chain-of-thought prompting techniques
- ✓benchmark-focused organizations standardizing model evaluation protocols
- ✓data scientists performing exploratory analysis on benchmark datasets
- ✓ML engineers building reproducible training pipelines with version control
- ✓teams using distributed computing frameworks (Spark, Dask) for large-scale evaluation
- ✓organizations standardizing on open data formats for interoperability
Known Limitations
- ⚠monolingual English-only dataset — no multilingual coverage for non-English math education contexts
- ⚠grade-school scope only — does not include algebra, geometry, calculus, or advanced mathematics
- ⚠crowdsourced annotations may have inconsistent solution quality or formatting across examples
- ⚠fixed dataset size (8,522 problems) limits ability to evaluate models on novel unseen problem distributions
- ⚠no temporal or difficulty stratification metadata — cannot easily filter by problem complexity level
- ⚠parquet format requires additional dependencies (pyarrow/fastparquet) not included in base Python
Requirements
Input / Output
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gsm8k — a dataset on HuggingFace with 8,22,680 downloads
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