MathQA
MathQA is a large-scale benchmark consisting of 37K English multiple-choice math word problems across diverse domains such as probability and geometry. It is designed to assess an LLM's capability for multi-step mathematical reasoning. To learn more about the dataset and its construction, you can read the original MathQA paper here.
Arguments
There are TWO optional arguments when using the MathQA benchmark:
- [Optional]
tasks: a list of tasks (MathQATaskenums), which specifies the subject areas for model evaluation. By default, this is set to all tasks. The list ofMathQATaskenums can be found here. - [Optional]
n_shots: the number of examples for few-shot learning. This is set to 5 by default and cannot exceed 5.
Usage
The code below assesses a custom mistral_7b model (click here to learn how to use ANY custom LLM) on geometry and probability in MathQA using 3-shot prompting.
from deepeval.benchmarks import MathQA
from deepeval.benchmarks.tasks import MathQATask
# Define benchmark with specific tasks and shots
benchmark = MathQA(
tasks=[MathQATask.PROBABILITY, MathQATask.GEOMETRY],
n_shots=3
)
# Replace 'mistral_7b' with your own custom model
benchmark.evaluate(model=mistral_7b)
print(benchmark.overall_score)The overall_score for this benchmark ranges from 0 to 1, where 1 signifies perfect performance and 0 indicates no correct answers. The model's score, based on exact matching, is calculated by determining the proportion of questions for which the model produces the precise correct multiple choice answer (e.g. 'A' or ‘C’) in relation to the total number of questions.
MathQA Tasks
The MathQATask enum classifies the diverse range of categories covered in the MathQA benchmark.
from deepeval.benchmarks.tasks import MathQATask
math_qa_tasks = [MathQATask.PROBABILITY]Below is the comprehensive list of available tasks:
PROBABILITYGEOMETRYPHYSICSGAINGENERALOTHER
FAQs
What does the MathQA benchmark measure?
How is MathQA scored?
overall_score is the proportion of questions for which the model produces the precise correct multiple-choice answer (e.g. 'A' or 'C') out of the total. It ranges from 0 to 1, where 1 signifies perfect performance.What is the default n_shots for MathQA?
5 and cannot exceed that. Use the n_shots argument to lower it for few-shot prompting.Which tasks can I evaluate with MathQA?
MathQATask enums to the tasks argument to target specific subject areas. Available tasks are PROBABILITY, GEOMETRY, PHYSICS, GAIN, GENERAL, and OTHER. By default, all tasks are evaluated.Can using more few-shot prompts improve the MathQA score?
n_shots can greatly improve the model's robustness in generating answers in the exact correct format and boost the overall_score.How do I run MathQA on a custom LLM?
evaluate() with your model and read overall_score. See benchmarking your LLM to learn how to use any custom LLM.