2026-07-08 08:57:50 +00:00

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# DocMath
## Overview
DocMath-Eval is a comprehensive benchmark focused on numerical reasoning within specialized domains. It requires models to comprehend long and specialized documents and perform numerical reasoning to answer questions.
## Task Description
- **Task Type**: Document-based Mathematical Reasoning
- **Input**: Long document context + numerical reasoning question
- **Output**: Numerical answer with reasoning
- **Focus**: Long-context comprehension and quantitative reasoning
## Key Features
- Long specialized documents requiring comprehension
- Numerical reasoning within document context
- Multiple complexity levels (comp/simp, long/short)
- Tests real-world document understanding
- Requires both reading comprehension and math skills
## Evaluation Notes
- Default configuration uses **0-shot** evaluation
- Uses LLM-as-judge for answer evaluation
- Subsets: complong_testmini, compshort_testmini, simplong_testmini, simpshort_testmini
- Answer format: "Therefore, the answer is (answer)"
## Properties
| Property | Value |
|----------|-------|
| **Benchmark Name** | `docmath` |
| **Dataset ID** | [yale-nlp/DocMath-Eval](https://modelscope.cn/datasets/yale-nlp/DocMath-Eval/summary) |
| **Paper** | N/A |
| **Tags** | `LongContext`, `Math`, `Reasoning` |
| **Metrics** | `acc` |
| **Default Shots** | 0-shot |
| **Evaluation Split** | `test` |
## Data Statistics
| Metric | Value |
|--------|-------|
| Total Samples | 800 |
| Prompt Length (Mean) | 68791.03 chars |
| Prompt Length (Min/Max) | 505 / 1009038 chars |
**Per-Subset Statistics:**
| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
|--------|---------|-------------|------------|------------|
| `complong_testmini` | 300 | 175355.17 | 18687 | 1009038 |
| `compshort_testmini` | 200 | 1990.74 | 505 | 9460 |
| `simplong_testmini` | 100 | 13972.84 | 6870 | 24001 |
| `simpshort_testmini` | 200 | 3154.2 | 560 | 9600 |
## Sample Example
**Subset**: `complong_testmini`
```json
{
"input": [
{
"id": "a07cbfcf",
"content": "Please read the following text and answer the question below.\n\n<text>\nDELTA AIR LINES, INC.\nConsolidated Balance Sheets\n| (in millions, except share data) | March 31, 2018 | December 31, 2017 |\n| ASSETS |\n| Current Assets: |\n| Cash and cash e ... [TRUNCATED] ... comprehensive income for foreign currency exchange contracts in 2017 and 2018, and the changes in value for derivative contracts and other in 2018, in million?\n\nFormat your response as follows: \"Therefore, the answer is (insert answer here)\"."
}
],
"target": "-31.0",
"id": 0,
"group_id": 0,
"metadata": {
"question_id": "complong-testmini-0",
"answer_type": "float"
}
}
```
*Note: Some content was truncated for display.*
## Prompt Template
**Prompt Template:**
```text
Please read the following text and answer the question below.
<text>
{context}
</text>
{question}
Format your response as follows: "Therefore, the answer is (insert answer here)".
```
## Usage
### Using CLI
```bash
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets docmath \
--limit 10 # Remove this line for formal evaluation
```
### Using Python
```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=['docmath'],
dataset_args={
'docmath': {
# subset_list: ['complong_testmini', 'compshort_testmini', 'simplong_testmini'] # optional, evaluate specific subsets
}
},
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)
```