Omkar2118/datasets huggingface dataset:
Educational Grade School Math (EGSM)

Educational Grade School Math (EGSM) contains 2,093 question/answer pairs generated by MATHWELL, a reference-free educational grade school math word problem generator that outputs a word problem and Program of Thought (PoT) solution based solely on an optional student interest, as introduced in MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations. The question/answer pairs are verified by human experts. EGSM is the first teacher-annotated math word problem training dataset for LLMs.

EGSM is designed to train reference-free educational grade school math word problem generators, but can also be used to train math QA models. Please refer to our paper for more information on the dataset.

Introducing paper: MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations
MATHWELL Human Annotation Dataset

The MATHWELL Human Annotation Dataset contains 5,084 synthetic word problems and answers generated by MATHWELL, a reference-free educational grade school math word problem generator released in MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations, and comparison models (GPT-4, GPT-3.5, Llama-2, MAmmoTH, and LLEMMA) with expert human annotations for solvability, accuracy, appropriateness, and meets all criteria (MaC). Solvability means the problem is mathematically possible to solve, accuracy means the Program of Thought (PoT) solution arrives at the correct answer, appropriateness means that the mathematical topic is familiar to a grade school student and the question's context is appropriate for a young learner, and MaC denotes questions which are labeled as solvable, accurate, and appropriate. Null values for accuracy and appropriateness indicate a question labeled as unsolvable, which means it cannot have an accurate solution and is automatically inappropriate. Based on our annotations, 82.2% of the question/answer pairs are solvable, 87.3% have accurate solutions, 78.1% are appropriate, and 58.4% meet all criteria.

This dataset is designed to train text classifiers to automatically label word problem generator outputs for solvability, accuracy, and appropriateness. More details about the dataset can be found in our paper.

Introducing paper: MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations