GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.

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We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.

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For the socratic configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained here), and Socratic sub-questions.

For the main configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained here).

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answer: The full solution string to the question. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.

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This dataset is generally used to test logic and math in language modelling. It has been used for many benchmarks, including the LLM Leaderboard.

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