README.md

---
license: cc-by-nc-sa-4.0
pretty_name: INTERCHART
tags:
- charts
- visualization
- vqa
- multimodal
- question-answering
- reasoning
- benchmarking
- evaluation
task_categories:
- question-answering
- visual-question-answering
task_ids:
- visual-question-answering
language:
- en
dataset_info:
  features:
  - name: id
    dtype: string
  - name: subset
    dtype: string
  - name: context_format
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: images
    sequence: string
  - name: metadata
    dtype: json
pretty_description: >
  INTERCHART is a diagnostic benchmark for multi-chart visual reasoning across three tiers:
  DECAF (decomposed single-entity charts), SPECTRA (synthetic paired charts for correlated trends),
  and STORM (real-world chart pairs). The dataset includes chart images and question–answer pairs
  designed to stress-test cross-chart reasoning, trend correlation, and abstract numerical inference.
---

# INTERCHART: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

[![Website](https://img.shields.io/badge/Website-InterChart.github.io-blue)](https://coral-lab-asu.github.io/interchart/)
[![Paper](https://img.shields.io/badge/arXiv-2508.07630v1-b31b1b)](https://arxiv.org/abs/2508.07630v1)
[![License](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-green)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

---

## 🧩 Overview

**INTERCHART** is a multi-tier benchmark that evaluates how well **vision-language models (VLMs)** reason across **multiple related charts**, a crucial skill for real-world applications like scientific reports, financial analyses, and policy dashboards.  
Unlike single-chart benchmarks, INTERCHART challenges models to integrate information across **decomposed**, **synthetic**, and **real-world** chart contexts.

> **Paper:** [INTERCHART: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information](https://arxiv.org/abs/2508.07630v1)

---

## 📂 Dataset Structure

```

INTERCHART/
├── DECAF
│   ├── combined       # Multi-chart combined images (stitched)
│   ├── original       # Original compound charts
│   ├── questions      # QA pairs for decomposed single-variable charts
│   └── simple         # Simplified decomposed charts
├── SPECTRA
│   ├── combined       # Synthetic chart pairs (shared axes)
│   ├── questions      # QA pairs for correlated and independent reasoning
│   └── simple         # Individual charts rendered from synthetic tables
├── STORM
│   ├── combined       # Real-world chart pairs (stitched)
│   ├── images         # Original Our World in Data charts
│   ├── meta-data      # Extracted metadata and semantic pairings
│   ├── questions      # QA pairs for temporal, cross-domain reasoning
│   └── tables         # Structured table representations (optional)

````

Each subset targets a different **level of reasoning complexity** and visual diversity.

---

## 🧠 Subset Descriptions

### **1️⃣ DECAF** — *Decomposed Elementary Charts with Answerable Facts*
- Focus: **Factual lookup** and **comparative reasoning** on simplified single-variable charts.  
- Sources: Derived from ChartQA, ChartLlama, ChartInfo, DVQA.  
- Content: 1,188 decomposed charts and 2,809 QA pairs.  
- Tasks: Identify, compare, or extract values across clean, minimal visuals.

---

### **2️⃣ SPECTRA** — *Synthetic Plots for Event-based Correlated Trend Reasoning and Analysis*
- Focus: **Trend correlation** and **scenario-based inference** between synthetic chart pairs.  
- Construction: Generated via Gemini 1.5 Pro + human validation to preserve shared axes and realism.  
- Content: 870 unique charts, 1,717 QA pairs across 333 contexts.  
- Tasks: Analyze multi-variable relationships, infer trends, and reason about co-evolving variables.

---

### **3️⃣ STORM** — *Sequential Temporal Reasoning Over Real-world Multi-domain Charts*
- Focus: **Multi-step reasoning**, **temporal analysis**, and **semantic alignment** across real-world charts.  
- Source: Curated from *Our World in Data* with metadata-driven semantic pairing.  
- Content: 648 charts across 324 validated contexts, 768 QA pairs.  
- Tasks: Align mismatched domains, estimate ranges, and reason about evolving trends.

---

## ⚙️ Evaluation & Methodology

INTERCHART supports both **visual** and **table-based** evaluation modes.

- **Visual Inputs:**  
  - *Combined:* Charts stitched into a unified image.  
  - *Interleaved:* Charts provided sequentially.  

- **Structured Table Inputs:**  
  Models can extract tables using tools like **DePlot** or **Gemini Title Extraction**, followed by **table-based QA**.

- **Prompting Strategies:**  
  - Zero-Shot  
  - Zero-Shot Chain-of-Thought (CoT)  
  - Few-Shot CoT with Directives (CoTD)

- **Evaluation Pipeline:**  
  Multi-LLM *semantic judging* (Gemini 1.5 Flash, Phi-4, Qwen2.5) with **majority voting** to evaluate semantic correctness.

---

## 📊 Dataset Statistics

| Subset  | Charts | Contexts | QA Pairs | Reasoning Type Examples |
|----------|---------|-----------|-----------|--------------------------|
| **DECAF** | 1,188 | 355 | 2,809 | Factual lookup, comparison |
| **SPECTRA** | 870 | 333 | 1,717 | Trend correlation, event reasoning |
| **STORM** | 648 | 324 | 768 | Temporal reasoning, abstract numerical inference |
| **Total** | 2,706 | 1,012 | **5,214** | — |

---

## 🚀 Usage

### 🔐 Access & Download Instructions

Use an **access token** as your Git credential when cloning or pushing to the repository.

1. **Install Git LFS**  
   Download and install from [https://git-lfs.com](https://git-lfs.com).  
   Then run:
```

git lfs install

```

2. **Clone the dataset repository**  
When prompted for a password, use your **Hugging Face access token** with *write permissions*.  
You can generate one here: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)

```

git clone [https://huggingface.co/datasets/interchart/Interchart](https://huggingface.co/datasets/interchart/Interchart)

```

3. **Clone without large files (LFS pointers only)**  
If you only want lightweight clones without downloading all image data:
```

GIT_LFS_SKIP_SMUDGE=1 git clone [https://huggingface.co/datasets/interchart/Interchart](https://huggingface.co/datasets/interchart/Interchart)

```

4. **Alternative: use the Hugging Face CLI**
Make sure the CLI is installed:
```

pip install -U "huggingface_hub[cli]"

```

Then download directly:
```

hf download interchart/Interchart --repo-type=dataset

```



---

## 🔍 Citation

If you use this dataset, please cite:

```
@article{iyengar2025interchart,
  title={INTERCHART: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information},
  author={Anirudh Iyengar Kaniyar Narayana Iyengar and Srija Mukhopadhyay and Adnan Qidwai and Shubhankar Singh and Dan Roth and Vivek Gupta},
  journal={arXiv preprint arXiv:2508.07630},
  year={2025}
}
```

---

## 🔗 Links

* 📘 **Paper:** [arXiv:2508.07630v1](https://arxiv.org/abs/2508.07630v1)
* 🌐 **Website:** [https://coral-lab-asu.github.io/interchart/](https://coral-lab-asu.github.io/interchart/)
* 🧠 **Explore Dataset:** [Interactive Evaluation Portal](https://coral-lab-asu.github.io/interchart/explore.html)