CharXiv

Overview

CharXiv is a comprehensive chart understanding benchmark from NeurIPS 2024 that evaluates multimodal large language models on realistic scientific charts from arXiv papers. It tests both low-level chart element perception (descriptive) and high-level reasoning about chart data.

Task Description

• Task Type: Chart Understanding (Descriptive + Reasoning)

• Input: Scientific chart image + question

• Output: Free-form text answer

• Domains: cs, physics, math, eess, q-bio, q-fin, stat, econ

Key Features

• 2,323 real scientific charts from arXiv papers across 8 disciplines

• Two question types:

  • Descriptive (4 per chart): Basic element identification (titles, axes, legends, trends, etc.)

  • Reasoning (1 per chart): Higher-order reasoning requiring data synthesis

• 19 descriptive question templates covering information extraction, enumeration, pattern recognition, counting, and compositionality

• 4 reasoning answer types: text-in-chart, text-in-general, number-in-chart, number-in-general

• Validation set (1,000 charts) and test set (1,323 charts)

• Evaluation via LLM judge following the official CharXiv grading protocol

Evaluation Notes

• Default evaluation uses the validation split (1,000 charts, 5,000 questions)

• Each chart yields 5 samples: 4 descriptive + 1 reasoning

• Primary metric: Accuracy via LLM-as-judge

• Subsets: descriptive and reasoning (also by category)

• Requires judge_model_args configuration for LLM judge

• Paper | GitHub

Properties

Property	Value
Benchmark Name	charxiv
Dataset ID	princeton-nlp/CharXiv
Paper	Paper
Tags	MultiModal, QA, Reasoning
Metrics	acc
Default Shots	0-shot
Evaluation Split	validation

Data Statistics

Metric	Value
Total Samples	5,000
Prompt Length (Mean)	276.24 chars
Prompt Length (Min/Max)	80 / 687 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
descriptive	4,000	261.51	156	432
reasoning	1,000	335.14	80	687

Image Statistics:

Metric	Value
Total Images	5,000
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	1023x139 - 1024x1024
Formats	jpeg

Sample Example

Subset: descriptive

{
  "input": [
    {
      "id": "44ff5b8a",
      "content": [
        {
          "image": "[BASE64_IMAGE: jpeg, ~70.0KB]"
        },
        {
          "text": "For the current plot, what is the spatially highest labeled tick on the y-axis?\n* Your final answer should be the tick value on the y-axis that is explicitly written. Ignore units or scales that are written separately from the tick."
        }
      ]
    }
  ],
  "target": "60",
  "id": 0,
  "group_id": 0,
  "subset_key": "descriptive",
  "metadata": {
    "question_type": "descriptive",
    "question_id": 7,
    "category": "cs",
    "original_id": "2004.10956"
  }
}

Prompt Template

No prompt template defined.

Usage

Using CLI

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

run_task(task_cfg=task_cfg)