--- 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) --- ## πŸ“‚ Dataset Structure ``` INTERCHART/ β”œβ”€β”€ DECAF β”‚ β”œβ”€β”€ combined # Multi-chart combined images (stitched) β”‚ β”œβ”€β”€ original # Original compound charts β”‚ β”œβ”€β”€ questions # QA pairs for decomposed single-variable charts β”‚ └── simple # Simplified decomposed charts β”œβ”€β”€ SPECTRA β”‚ β”œβ”€β”€ combined # Synthetic chart pairs (shared axes) β”‚ β”œβ”€β”€ questions # QA pairs for correlated and independent reasoning β”‚ └── simple # Individual charts rendered from synthetic tables β”œβ”€β”€ STORM β”‚ β”œβ”€β”€ combined # Real-world chart pairs (stitched) β”‚ β”œβ”€β”€ images # Original Our World in Data charts β”‚ β”œβ”€β”€ meta-data # Extracted metadata and semantic pairings β”‚ β”œβ”€β”€ questions # QA pairs for temporal, cross-domain reasoning β”‚ └── tables # Structured table representations (optional) ```` Each subset targets a different **level of reasoning complexity** and visual diversity. --- ## 🧠 Subset Descriptions ### **1️⃣ DECAF** β€” *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** β€” *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** β€” *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. --- ## βš™οΈ 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. --- ## πŸ“Š 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** | β€” | --- ## πŸš€ Usage ### πŸ” 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 ``` --- ## πŸ” 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} } ``` --- ## πŸ”— Links * πŸ“˜ **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)