README.md

---
language:
- en
license: mit
task_categories:
- image-text-to-text
tags:
- MICL
- MLLMs
- in-context-learning
- vision-language
---

# TrueMICL: True Multimodal In-Context Learning Dataset

A comprehensive multimodal dataset designed to evaluate and improve true multimodal in-context learning capabilities in Multimodal Large Language Models (MLLMs).

[Paper](https://huggingface.co/papers/2507.15807) | [Code](https://github.com/chenxshuo/true-micl-colm) | [Project page](https://chenxshuo.github.io/true-micl-colm)

## Table of Contents
- [Dataset Overview](#dataset-overview)
- [Dataset Structure](#dataset-structure)
- [Tasks and Domains](#tasks-and-domains)
- [Usage Examples](#usage-examples)
- [Data Collection Methodology](#data-collection-methodology)
- [Citation](#citation)
- [License](#license)
- [Contact](#contact)

## Dataset Overview

TrueMICL addresses a critical limitation in current Multimodal Large Language Models: their tendency to neglect visual information in multimodal demonstrations, leading to superficial text imitation. This dataset is specifically designed to test **true** multimodal in-context learning by ensuring that:

- Tasks are unsolvable without visual context
- Novel image-text relationships are introduced
- Visual information is perceivable and critical
- Compatibility with language model backbones is maintained

### Key Statistics
- **Total samples**: 867 evaluation samples + extensive training data
- **Task categories**: 4 major categories
- **Distinct tasks**: 7 different tasks
- **Domains**: Mathematical reasoning, pattern recognition, concept learning, visual question answering

## Dataset Structure

The dataset is organized into task-specific directories, each containing:

### File Organization
```
dataset/
├── classification/          # Character classification task
│   ├── img/                # Query and support images
│   ├── query.json          # Test queries 
│   └── support.json        # Support examples 
├── clevr/                  # CLEVR-based reasoning tasks
│   ├── material/           # Material-based images
│   ├── query/              # Query images
│   ├── shape/              # Shape-based images
│   ├── size/               # Size-based images
│   ├── support/            # Support images
│   ├── query.json          # Main queries 
│   ├── support.json        # Support examples 
│   └── [query/support]_[material/shape/size].json  # Task-specific splits
├── clock/                  # Clock reading and math
│   ├── img/                # Clock face images
│   ├── query.json          # Test queries 
│   └── support.json        # Support examples
├── operator_induction/     # Mathematical operator learning
│   ├── query.json          # Test queries 
│   ├── support.json        # Support examples 
│   └── processed_training_data.json  # Training data
├── palindrome_dataset/     # Palindrome pattern recognition
│   ├── query.json          # Test queries 
│   ├── support.json        # Support examples 
│   └── training_data.json  # Training data
├── shapes_count/           # Shape counting task
│   ├── query.json          # Test queries 
│   ├── support.json        # Support examples 
│   └── training_data.json  # Training data 
├── sudoku/                 # Sudoku puzzle solving
│   ├── query.json          # Test queries 
│   └── support.json        # Support examples 
└── vqav2/                  # Visual Question Answering v2
    ├── query.json          # Test queries 
    └── support.json        # Support examples 
```

### Data Format

Each JSON file contains structured data with the following schema:

**Query/Support Format**:
```json
{
  "id": "unique_identifier",
  "image": ["path/to/image.png"],
  "question": "Question text with multiple choice options",
  "answer": "Correct answer"
}
```

**VQA Format** (slightly different):
```json
{
  "image_id": 12345,
  "question_id": 67890,
  "question": "Question text",
  "answer": "Answer text"
}
```

### Data Types and Columns

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique identifier for the sample |
| `image` | array | List of image file paths |
| `question` | string | Question or task description |
| `answer` | string | Ground truth answer |
| `image_id` | integer | Image identifier (VQA format) |
| `question_id` | integer | Question identifier (VQA format) |

## Tasks and Domains

### 1. Mathematical Reasoning
- **Operator Induction**: Learn novel mathematical operators from visual examples
- **Clock Math**: Time reading and calculation tasks

### 2. Concept Binding  
- **Character Classification**: Classify novel character types from visual examples
- **CLEVR Count**: Object counting and attribute reasoning

### 3. Pattern Finding
- **Sudoku**: Complete Sudoku puzzles using visual pattern recognition
- **Palindrome**: Identify palindromic patterns in visual sequences

### 4. Novel Concept Learning
- **Shapes Count**: Count specific shapes and understand spatial relationships
- **VQA**: General visual question answering requiring multimodal reasoning

## Usage Examples

### Basic Data Exploration

```python
import json
import matplotlib.pyplot as plt
from PIL import Image

# Load and examine a sample
with open("classification/query.json", "r") as f:
    data = json.load(f)

sample = data[0]
print(f"ID: {sample['id']}")
print(f"Question: {sample['question']}")
print(f"Answer: {sample['answer']}")

# Load and display the image
img_path = sample['image'][0]
img = Image.open(img_path)
plt.imshow(img)
plt.title(sample['question'])
plt.show()
```

### Task-Specific Loading

```python
# Load CLEVR subtasks
clevr_tasks = ['material', 'shape', 'size']
for task in clevr_tasks:
    with open(f"clevr/query_{task}.json", "r") as f:
        task_data = json.load(f)
    print(f"CLEVR {task}: {len(task_data)} samples")
```

## Data Collection Methodology

The dataset was constructed following rigorous criteria to ensure true multimodal learning:

1. **Visual Dependency**: All tasks require visual information and cannot be solved through text-only reasoning
2. **Novel Relationships**: Introduction of previously unseen image-text mappings
3. **Perceptual Validity**: Visual elements are clearly perceivable and unambiguous
4. **Model Compatibility**: Designed to work with standard language model architectures

### Source Data
- **CLEVR**: Modified from the original CLEVR dataset for visual reasoning
- **VQAv2**: Subset of the Visual Question Answering v2 dataset
- **Synthetic Tasks**: Custom-generated tasks for operator induction, palindromes, and shape counting
- **Novel Concepts**: Artificially created character types and visual patterns

## Citation

```bibtex
@inproceedings{wu2024fiva,
      title={True Multimodal In-Context Learning Needs Attention to the Visual Context},
      author={Tong Wu and Yinghao Xu and Ryan Po and Mengchen Zhang and Guandao Yang and Jiaqi Wang and Ziwei Liu and Dahua Lin and Gordon Wetzstein},
      booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
      year={2024},
      url={https://openreview.net/forum?id=Vp6HAjrdIg}
      }
```

## License

This dataset is released under the [MIT License](LICENSE). Please see the license file for detailed terms and conditions.

## Contact

For questions, issues, or contributions regarding this dataset:

- **Project Website**: https://chenxshuo.github.io/true-micl-colm/
- **Paper**: https://huggingface.co/papers/2507.15807
- **Code**: https://github.com/chenxshuo/true-micl-colm
- **Issues**: Please report bugs or request features through the appropriate channels

---

**Note**: This dataset is designed for research purposes to advance multimodal in-context learning. The novel tasks and visual concepts are specifically crafted to test true multimodal understanding rather than superficial pattern matching.