13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
120 lines
3.3 KiB
Markdown
120 lines
3.3 KiB
Markdown
# CharXiv
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## Overview
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CharXiv is a comprehensive chart understanding benchmark from NeurIPS 2024 that evaluates multimodal
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large language models on realistic scientific charts from arXiv papers. It tests both low-level
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chart element perception (descriptive) and high-level reasoning about chart data.
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## Task Description
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- **Task Type**: Chart Understanding (Descriptive + Reasoning)
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- **Input**: Scientific chart image + question
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- **Output**: Free-form text answer
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- **Domains**: cs, physics, math, eess, q-bio, q-fin, stat, econ
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## Key Features
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- 2,323 real scientific charts from arXiv papers across 8 disciplines
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- Two question types:
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- **Descriptive** (4 per chart): Basic element identification (titles, axes, legends, trends, etc.)
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- **Reasoning** (1 per chart): Higher-order reasoning requiring data synthesis
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- 19 descriptive question templates covering information extraction, enumeration, pattern recognition, counting, and compositionality
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- 4 reasoning answer types: text-in-chart, text-in-general, number-in-chart, number-in-general
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- Validation set (1,000 charts) and test set (1,323 charts)
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- Evaluation via LLM judge following the official CharXiv grading protocol
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## Evaluation Notes
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- Default evaluation uses the **validation** split (1,000 charts, 5,000 questions)
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- Each chart yields 5 samples: 4 descriptive + 1 reasoning
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- Primary metric: **Accuracy** via LLM-as-judge
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- Subsets: `descriptive` and `reasoning` (also by category)
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- Requires an LLM judge configured through `judge.models`
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- [Paper](https://arxiv.org/abs/2406.18521) | [GitHub](https://github.com/princeton-nlp/CharXiv)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `charxiv` |
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| **Dataset ID** | [princeton-nlp/CharXiv](https://modelscope.cn/datasets/princeton-nlp/CharXiv/summary) |
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| **Paper** | [Paper](https://arxiv.org/abs/2406.18521) |
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| **Tags** | `MultiModal`, `QA`, `Reasoning` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `validation` |
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## Data Statistics
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*Statistics not available.*
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## Sample Example
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**Subset**: `descriptive`
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```json
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{
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"input": [
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{
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"id": "dd588b4d",
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"content": [
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{
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"image": "[BASE64_IMAGE: jpeg, ~70.0KB]"
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},
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{
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"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."
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}
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]
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}
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],
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"target": "60",
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"id": 0,
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"group_id": 0,
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"subset_key": "descriptive",
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"metadata": {
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"question_type": "descriptive",
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"question_id": 7,
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"category": "cs",
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"original_id": "2004.10956"
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}
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}
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```
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## Prompt Template
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*No prompt template defined.*
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets charxiv \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['charxiv'],
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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