Datasets:
Tasks:
Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
math
License:
File size: 2,777 Bytes
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---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- math
---
## Overview
VCBench provides a standardized framework for evaluating vision-language models. This document outlines the procedures for both standard evaluation and GPT-assisted evaluation of your model's outputs.
## 1. Standard Evaluation
### 1.1 Output Format Requirements
Models must produce outputs in JSONL format with the following structure:
```
{"id": <int>, "pred_answer": "<answer_letter>"}
{"id": <int>, "pred_answer": "<answer_letter>"}
...
```
**Example File (`submit.jsonl`):**
```json
{"id": 1, "pred_answer": "A"}
{"id": 2, "pred_answer": "B"}
{"id": 3, "pred_answer": "C"}
```
### 1.2 Evaluation Procedure
1. Ensure your predictions file follows the specified format
2. Run the evaluation script:
```bash
python evaluate_vcbench.py -p ./path/to/predictions.jsonl -g ./path/to/VCBench_with_answer.json
```
`VCBench_with_answer.json` is the ground truth file which can be downloaded from [here](https://huggingface.co/datasets/cloudcatcher2/VCBench/resolve/main/VCBench_with_answer.json).
## 2. GPT-Assisted Evaluation
### 2.1 Output Format Requirements
For natural language responses, use this JSONL format:
```
{"id": <int>, "pred_answer": "<natural_language_response>"}
{"id": <int>, "pred_answer": "<natural_language_response>"}
...
```
**Example File (`nl_predictions.jsonl`):**
```json
{"id": 1, "pred_answer": "The correct answer is A"}
{"id": 2, "pred_answer": "After careful analysis, option B appears correct"}
{"id": 3, "pred_answer": "C is the right choice"}
```
### 2.2 Environment Setup
Set your Dashscope API key:
```bash
export DASHSCOPE_KEY="your_api_key_here"
```
### 2.3 Evaluation Procedure
```bash
python evaluate_vcbench_by_gpt.py -p ./path/to/nl_predictions.jsonl -g ./path/to/VCBench_with_answer.json
```
## 3. Expected Output
Both evaluation scripts will provide:
- Overall accuracy percentage
- Per-question-type accuracy breakdown
- Progress updates during evaluation
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```bibtex
@misc{wong2025vcbench
author = {Zhikai Wang and Jiashuo Sun and Wenqi Zhang and Zhiqiang Hu and Xin Li and Fan Wang and Deli Zhao},
title = {Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency},
year = {2025},
eprint = {2504.18589},
archivePrefix = {arxiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2504.18589}
}
```
## Dataset Card Authors
- [Zhikai Wang](https://cloudcatcher888.github.io/): [email protected]
- [Jiashuo Sun](https://gasolsun36.github.io/): [email protected]
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