Datasets:

ShuoChen99
/

TrueMICL

	---
	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.