---
language:
- en
license: apache-2.0
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tags:
- code
---

## HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks
<p align="left">
    <a href="https://huggingface.co/papers/2410.12381">📄 Paper </a> •
    <a href="https://humaneval-v.github.io">🏠 Home Page</a> •
    <a href="https://github.com/HumanEval-V/HumanEval-V-Benchmark">💻 GitHub Repository </a> •
    <a href="https://humaneval-v.github.io/#leaderboard">🏆 Leaderboard</a> •
    <a href="https://huggingface.co/spaces/HumanEval-V/HumanEval-V-Benchmark-Viewer">🤗 Dataset Viewer</a> 
</p>

**HumanEval-V** is a novel benchmark designed to evaluate the diagram understanding and reasoning capabilities of Large Multimodal Models (LMMs) in programming contexts. Unlike existing benchmarks, HumanEval-V focuses on coding tasks that require sophisticated visual reasoning over complex diagrams, pushing the boundaries of LMMs' ability to comprehend and process visual information. The dataset includes **253 human-annotated Python coding tasks**, each featuring a critical, self-explanatory diagram with minimal textual clues. These tasks require LMMs to generate Python code based on the visual context and predefined function signatures.


<div style="text-align: center;">
<img src="task_example.png" alt="" width="650"/>
</div>

## Key features:
- **Complex diagram understanding** that is indispensable for solving coding tasks.
- **Real-world problem contexts** with diverse diagram types and spatial reasoning challenges.
- **Code generation tasks**, moving beyond multiple-choice or short-answer questions to evaluate deeper visual and logical reasoning capabilities.
- **Two-stage evaluation pipeline** that separates diagram description generation and code implementation for more accurate visual reasoning assessment.
- **Handcrafted test cases** for rigorous execution-based evaluation through the **pass@k** metric.


<div style="text-align: center;">
<img src="task_type_and_capability_aspects.png" alt="" width="1000"/>
</div>


## Dataset Structure
Each task in the dataset consists of the following fields:

- **qid**: A unique identifier for each coding task (e.g., _q1_, with mutated versions like _q1-2_, _q1-3_).
- **diagram**: A single diagram that provides the essential visual context required to solve the task.
- **function_signature**: Includes necessary imports and the function signature that the LMMs must complete.
- **test_script**: The test cases used to validate the correctness of the generated code.
- **ground_truth_solution**: The human-annotated code solutions for the task.
- **ground_truth_diagram_description**: Human-annotated descriptions of the diagram.
- **task_type**: The type of the task, which falls into one of six categories, as shown in **Figure 2**.
- **capability_aspects**: The capabilities required to understand the diagram in the task, which include seven dimensions and their sub-aspects, as shown in **Figure 3**.

## Usage
You can easily load the dataset using the Hugging Face `datasets` library.

```python
from datasets import load_dataset
humaneval_v = load_dataset("HumanEval-V/HumanEval-V-Benchmark", split="test")
```

## Citation
```bibtex
@article{zhang2024humanevalv,
  title={HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks}, 
  author={Zhang, Fengji and Wu, Linquan and Bai, Huiyu and Lin, Guancheng and Li, Xiao and Yu, Xiao and Wang, Yue and Chen, Bei and Keung, Jacky},
  journal={arXiv preprint arXiv:2410.12381},
  year={2024},
}
```