ChartQA

Overview

ChartQA is a benchmark designed to evaluate question-answering capabilities over charts and data visualizations. It tests both visual reasoning and logical understanding of various chart types including bar charts, line graphs, and pie charts.

Task Description

Task Type: Chart Question Answering
Input: Chart image + natural language question
Output: Single word or numerical answer
Domains: Data visualization, visual reasoning, numerical reasoning

Key Features

Covers diverse chart types (bar, line, pie, scatter plots)
Includes both human-written and augmented test questions
Requires understanding of chart structure and data relationships
Tests both visual extraction and logical reasoning abilities
Questions range from simple data lookup to complex reasoning

Evaluation Notes

Default evaluation uses the test split with two subsets:
- human_test: Human-written questions
- augmented_test: Automatically generated questions
Primary metric: Relaxed Accuracy (allows minor variations in answers)
Answers should be in format "ANSWER: [ANSWER]"
Numerical answers may have tolerance for rounding differences

Properties

Property	Value
Benchmark Name	`chartqa`
Dataset ID	lmms-lab/ChartQA
Paper	N/A
Tags	`Knowledge`, `MultiModal`, `QA`
Metrics	`relaxed_acc`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics

Metric	Value
Total Samples	2,500
Prompt Length (Mean)	224.33 chars
Prompt Length (Min/Max)	178 / 352 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`human_test`	1,250	220.17	178	352
`augmented_test`	1,250	228.49	186	293

Image Statistics:

Metric	Value
Total Images	2,500
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	184x326 - 800x1796
Formats	png

Sample Example

Subset: human_test

{
  "input": [
    {
      "id": "c75439e0",
      "content": [
        {
          "text": "\nHow many food item is shown in the bar graph?\n\nThe last line of your response should be of the form \"ANSWER: [ANSWER]\" (without quotes) where [ANSWER] is the a single word answer or number to the problem.\n"
        },
        {
          "image": "[BASE64_IMAGE: png, ~42.9KB]"
        }
      ]
    }
  ],
  "target": "14",
  "id": 0,
  "group_id": 0,
  "subset_key": "human_test"
}

Prompt Template

Prompt Template:


{question}

The last line of your response should be of the form "ANSWER: [ANSWER]" (without quotes) where [ANSWER] is the a single word answer or number to the problem.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets chartqa \
    --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=['chartqa'],
    dataset_args={
        'chartqa': {
            # subset_list: ['human_test', 'augmented_test']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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