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Interchart

Tasks:
Question Answering
Visual Question Answering
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visual-question-answering
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charts
visualization
vqa
multimodal
question-answering
reasoning
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license: creativeml-openrail-m

INTERCHART: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

Website Paper License

🧩 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

📂 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

Load from Hugging Face 

from datasets import load_dataset

dataset = load_dataset("interchart/interchart", name="DECAF")
print(dataset["train"][0])

Available subsets:

  • "DECAF"
  • "SPECTRA"
  • "STORM"

Each entry contains:

{
  "id": "DECAF_00123",
  "image_path": "DECAF/simple/chart_123.png",
  "question": "What is the highest bar value for 2020?",
  "answer": "45.6",
  "question_type": "comparison",
  "subset": "DECAF"
}

🔍 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