3.4 KiB
3.4 KiB
MathQA
Overview
MathQA is a large-scale dataset for mathematical word problem solving, gathered by annotating the AQuA-RAT dataset with fully-specified operational programs using a new representation language. It contains diverse math problems requiring multi-step reasoning.
Task Description
- Task Type: Mathematical Reasoning (Multiple-Choice)
- Input: Math word problem with multiple answer choices
- Output: Correct answer with chain-of-thought reasoning
- Difficulty: Varied (elementary to intermediate level)
Key Features
- Annotated with executable operational programs
- Tests quantitative reasoning and problem-solving skills
- Diverse mathematical topics and question formats
- Multiple-choice format with structured solutions
- Useful for evaluating mathematical reasoning capabilities
Evaluation Notes
- Default configuration uses 0-shot evaluation
- Uses Chain-of-Thought (CoT) prompting for reasoning
- Evaluates on test split
- Simple accuracy metric
- Reasoning steps available in metadata
Properties
| Property | Value |
|---|---|
| Benchmark Name | math_qa |
| Dataset ID | extraordinarylab/math-qa |
| Paper | N/A |
| Tags | MCQ, Math, Reasoning |
| Metrics | acc |
| Default Shots | 0-shot |
| Evaluation Split | test |
Data Statistics
| Metric | Value |
|---|---|
| Total Samples | 2,985 |
| Prompt Length (Mean) | 433.02 chars |
| Prompt Length (Min/Max) | 257 / 879 chars |
Sample Example
Subset: default
{
"input": [
{
"id": "e77fca06",
"content": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D,E. Think step by step before answering.\n\na shopkeeper sold an article offering a discount of 5 % and earned a profit of 31.1 % . what would have been the percentage of profit earned if no discount had been offered ?\n\nA) 38\nB) 27.675\nC) 30\nD) data inadequate\nE) none of these"
}
],
"choices": [
"38",
"27.675",
"30",
"data inadequate",
"none of these"
],
"target": "A",
"id": 0,
"group_id": 0,
"metadata": {
"reasoning": "\"giving no discount to customer implies selling the product on printed price . suppose the cost price of the article is 100 . then printed price = 100 ã — ( 100 + 31.1 ) / ( 100 â ˆ ’ 5 ) = 138 hence , required % profit = 138 â € “ 100 = 38 % answer a\""
}
}
Prompt Template
Prompt Template:
Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.
{question}
{choices}
Usage
Using CLI
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets math_qa \
--limit 10 # Remove this line for formal evaluation
Using Python
from evalscope import run_task
from evalscope.config import TaskConfig
task_cfg = TaskConfig(
model='YOUR_MODEL',
api_url='OPENAI_API_COMPAT_URL',
api_key='EMPTY_TOKEN',
datasets=['math_qa'],
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)