README.md

---
tags:
- Multimodal
dataset_info:
  features:
  - name: id
    dtype: string
  - name: foldername
    dtype: string
  - name: image1
    dtype: image
  - name: image2
    dtype: image
  - name: image3
    dtype: image
    split: ica
  - name: image4
    dtype: image
    split: ica
  - name: relation
    dtype: string
  - name: domain
    dtype: string
  - name: type
    dtype: string
  - name: culture
    dtype: string
  - name: language
    dtype: string
  - name: explanation
    dtype: string
    split: ria
  - name: hop_count
    dtype: int64
  - name: reasoning
    dtype: string
  - name: perception
    dtype: string
    split: ria
  - name: conception
    dtype: string
    split: ria
  - name: img_id1
    dtype: string
  - name: filename1
    dtype: string
  - name: description1
    dtype: string
  - name: image_path1
    dtype: string
  - name: img_id2
    dtype: string
  - name: filename2
    dtype: string
  - name: description2
    dtype: string
  - name: image_path2
    dtype: string
  - name: img_id3
    dtype: string
  - name: filename3
    dtype: string
  - name: description3
    dtype: string
    split: ica
  - name: image_path3
    dtype: string
    split: ica
  - name: img_id4
    dtype: string
    split: ica
  - name: filename4
    dtype: string
    split: ica
  - name: description4
    dtype: string
    split: ica
  - name: image_path4
    dtype: string
    split: ica
configs:
- config_name: default
  data_files:
  - split: ria
    path: data/ria-*
  - split: ica
    path: data/ica-*
license: cc-by-4.0
---

# MM-OPERA: Multi-Modal OPen-Ended Reasoning-guided Association Benchmark 🧠🌐

## Overview 📖

MM-OPERA is a benchmark designed to evaluate the open-ended association reasoning capabilities of Large Vision-Language Models (LVLMs). With 11,497 instances, it challenges models to identify and express meaningful connections across distant concepts in an open-ended format, mirroring human-like reasoning. The dataset spans diverse cultural, linguistic, and thematic contexts, making it a robust tool for advancing multimodal AI research. 🌍✨
<div style="text-align: center;">
  <img src="mm-opera-bench-statistics.jpg" width="80%">
</div>
<div style="text-align: center;">
  <img src="mm-opera-bench-overview.jpg" width="80%">
</div>

**Key Highlights**:

- **Tasks**: Remote-Item Association (RIA) and In-Context Association (ICA)
- **Dataset Size**: 11,497 instances (8021 in RIA, 869 × 4 = 3476 in ICA)
- **Context Coverage**: Multilingual, multicultural, and rich thematic contexts
- **Hierarchical Ability Taxonomy**: 13 associative ability dimensions (conception/perception) and 3 relationship types
- **Structured Clarity**: Association reasoning paths for clear and structured reasoning
- **Evaluation**: Open-ended responses assessed via tailored LLM-as-a-Judge with cascading scoring rubric and process-reward reasoning scoring
- **Applications**: Enhances LVLMs for real-world tasks like knowledge synthesis and relational inference

MM-OPERA is ideal for researchers and developers aiming to push the boundaries of multi-modal association reasoning. 🚀


## Why Open-Ended Association Reasoning? 🧠💡

**Association** is the backbone of human cognition, enabling us to connect disparate ideas, synthesize knowledge, and drive processes like memory, perception, creative thinking and rule discovery. While recent benchmarks explore association via closed-ended tasks with fixed options, they often fall short in capturing the dynamic reasoning needed for real-world AI. 😕

**Open-ended association reasoning** is the key to unlocking LVLMs' true potential. Here's why:

- 🚫 **No Bias from Fixed Options**: Closed-ended tasks can subtly guide models, masking their independent reasoning abilities.
- 🌟 **Complex, Multi-Step Challenges**: Open-ended formats allow for intricate, long-form reasoning, pushing models to tackle relational inference head-on.

These insights inspired MM-OPERA, a benchmark designed to rigorously evaluate and enhance LVLMs’ associative reasoning through open-ended tasks. Ready to explore the future of multimodal reasoning? 🚀


## Features 🔍

🧩 **Novel Tasks Aligned with Human Psychometric Principles**: 
- **RIA**: Links distant concepts through structured reasoning.
- **ICA**: Evaluates pattern recognition in in-context learning scenarios.

🌐 **Broad Coverage**: 13 associative ability dimensions, 3 relationship types, across multilingual (15 languages), multicultural contexts and 22 topic domains.

📊 **Rich Metrics**: Evaluates responses on Score Rate, Reasoning Score, Reasonableness, Distinctiveness, Knowledgeability, and more for nuanced insights.

✅ **Open-ended Evaluation**: Free-form responses with cascading scoring rubric, avoiding bias from predefined options.

📈 **Process-Reward Reasoning Evaluation**: Accesses each association reasoning step towards the final outcome connections, offering insights of reasoning process that outcome-based metrics cannot capture.

## Usage Example 💻

```python
from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("titic/MM-OPERA")

# Example of an RIA instance
ria_example = ds['ria'][0]
print(ria_example)

# Example of an ICA instance
ica_example = ds['ica'][0]
print(ica_example)
```

Explore MM-OPERA to unlock the next level of multimodal association reasoning! 🌟

## Citation ✏️

If you use this dataset in your work, please cite it as follows:

```bibtex
@misc{huang2025mmopera,
  author    = {Zimeng Huang and Jinxin Ke and Xiaoxuan Fan and Yufeng Yang and Yang Liu and Liu Zhonghan and Zedi Wang and Junteng Dai and Haoyi Jiang and Yuyu Zhou and Keze Wang and Ziliang Chen},
  title     = {MM-OPERA},
  month     = {oct},
  year      = {2025},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.17300924},
  url       = {https://doi.org/10.5281/zenodo.17300924}
}
```