Update README.md

README.md

@@ -9,4 +9,199 @@ tags:
 - 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).
+
+## 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 (656 samples)
+│   └── support.json        # Support examples (128 samples)
+├── 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 (368 samples)
+│   ├── support.json        # Support examples (1568 samples)
+│   └── [query/support]_[material/shape/size].json  # Task-specific splits
+├── clock/                  # Clock reading and math
+│   ├── img/                # Clock face images
+│   ├── query.json          # Test queries (900 samples)
+│   └── support.json        # Support examples (192 samples)
+├── operator_induction/     # Mathematical operator learning
+│   ├── query.json          # Test queries (900 samples)
+│   ├── support.json        # Support examples (192 samples)
+│   └── processed_training_data.json  # Training data (6300 samples)
+├── palindrome_dataset/     # Palindrome pattern recognition
+│   ├── query.json          # Test queries (800 samples)
+│   ├── support.json        # Support examples (256 samples)
+│   └── training_data.json  # Training data (3150 samples)
+├── shapes_count/           # Shape counting task
+│   ├── query.json          # Test queries (900 samples)
+│   ├── support.json        # Support examples (288 samples)
+│   └── training_data.json  # Training data (3150 samples)
+├── sudoku/                 # Sudoku puzzle solving
+│   ├── query.json          # Test queries (800 samples)
+│   └── support.json        # Support examples (128 samples)
+└── vqav2/                  # Visual Question Answering v2
+    ├── query.json          # Test queries (157,680 samples)
+    └── support.json        # Support examples (2,662,542 samples)
+```
+
+### 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
+
+## 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**: Available at the project website
+- **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.