language:
- en
license: apache-2.0
size_categories:
- n<1K
task_categories:
- image-text-to-text
dataset_info:
features:
- name: qid
dtype: string
- name: ground_truth_solution
dtype: string
- name: ground_truth_diagram_description
dtype: string
- name: test_script
dtype: string
- name: function_signature
dtype: string
- name: diagram
dtype: image
- name: capability_aspects
struct:
- name: Common Sense
sequence: string
- name: Data Structures
sequence: string
- name: Dynamic Patterns
sequence: string
- name: Geometric Objects
sequence: string
- name: Mathematical Operations
sequence: string
- name: Spatial Transformations
sequence: string
- name: Topological Relations
sequence: string
- name: task_type
dtype: string
splits:
- name: test
num_bytes: 32915902
num_examples: 253
download_size: 32012630
dataset_size: 32915902
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
tags:
- code
HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks
📄 Paper • 🏠 Home Page • 💻 GitHub Repository • 🏆 Leaderboard • 🤗 Dataset Viewer
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.
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.
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.
from datasets import load_dataset
humaneval_v = load_dataset("HumanEval-V/HumanEval-V-Benchmark", split="test")
Citation
@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},
}