README.md

---
dataset_info:
  config_name: tikz
  features:
  - name: difficulty_ast
    sequence: float32
  - name: id
    dtype: string
  - name: code
    dtype: string
  - name: commented_code
    dtype: string
  - name: instruction
    dtype: string
  - name: result_description
    dtype: string
  - name: difficulty
    dtype: string
  - name: modification_type
    dtype: string
  - name: type
    dtype: string
  - name: patch
    sequence: string
  - name: template_solution_code
    sequence: string
  - name: code_solution
    sequence: string
  - name: image_solution
    sequence: image
  - name: image_input
    dtype: image
  splits:
  - name: benchmark
    num_bytes: 7779734.0
    num_examples: 100
  - name: test
    num_bytes: 27150.0
    num_examples: 2
  download_size: 86778966
  dataset_size: 7806884.0
configs:
- config_name: tikz
  data_files:
  - split: benchmark
    path: tikz/benchmark-*
  - split: test
    path: tikz/test-*
---

# vTikZ: A Benchmark for Visually-Grounded Code Editing

[📄 Paper](https://arxiv.org/abs/2505.04670) • [💻 Code](https://github.com/IV2C/VTikZ) • [🌐 Website](https://iv2c.github.io/VTikZ/)

## Dataset Summary

**vTikZ** is the first benchmark explicitly designed to evaluate Large Language Models (LLMs) on code editing tasks with visual intent. It focuses on scenarios where natural language instructions are used to modify code that generates diagrams or figures, such as TikZ. The dataset targets the core challenges of this task: localizing relevant code (feature location), generating valid code variants, and ensuring visual consistency with user intent.

## Features

### Human-Annotated

* **Original Code**: The base diagram-generating code written by humans.
* **Parameterized Solution Code**: Templates capturing acceptable variations for a given customization.
* **Instruction**: Natural language directive describing the intended visual change.
* **Perceived Difficulty**: Subjective difficulty score assigned to each customization task.
* **Result Description**: Human-written description of the desired visual outcome.
* **Modification Type**: Type of code change—`add`, `remove`, or `update`.
* **Type**: Category of the original diagram (`scientific` or `animal`).

### Automatically Computed

* **Perfect Variants**: Set of code solutions generated from the parameterized template.
* **Patch**: Context-free unidiff patch between the original and perfect variant(s).
* **Image Input**: Rendered image from the original code.
* **Images Solution**: Rendered images of all perfect variants.
* **AST Difficulty**: Tree edit distance (TED) between original and variant code \[Zhang & Shasha, 1989].


## Citation

If you use this dataset, please cite:

```
@inproceedings{reux_llmvisualcutomization_2025,
author = {Reux, Charly and Acher, Mathieu and Khelladi, Djamel Eddine and Barais, Olivier and Quinton, Cl{'e}ment},
  title     = {LLM Code Customization with Visual Results: A Benchmark on TikZ},
	booktitle = {Proceedings of {The} 29th {International} {Conference} on {Evaluation} and {Assessment} in {Software} {Engineering} ({EASE} 2025).},
  year      = {2025},
}
```