Dataset Card for Mutlimodal Arxiv QA

Dataset Loading Instruction

Each line of the arxivqa.jsonl file is an example:

{"id": "cond-mat-2862",
"image": "images/0805.4509_1.jpg",
"options": ["A) The ordering temperatures for all materials are above the normalized temperature \\( T/T_c \\) of 1.2.", "B) The magnetic ordering temperatures decrease for Dy, Tb, and Ho as the normalized temperature \\( T/T_c \\) approaches 1.", "C) The magnetic ordering temperatures for all materials are the same across the normalized temperature \\( T/T_c \\).", "D) The magnetic ordering temperature is highest for Yttrium (Y) and decreases for Dy, Tb, and Ho."],
"question": "What can be inferred about the magnetic ordering temperatures of the materials tested as shown in the graph?",
"label": "B",
"rationale": "The graph shows a sharp decline in frequency as the normalized temperature \\( T/T_c \\) approaches 1 for Dy, Tb, and Ho, indicating that their magnetic ordering temperatures decrease. No such data is shown for Yttrium (Y), thus we can't infer it has the highest magnetic ordering temperature."
}

• Download the arxivqa.json and images.tgz to your machine.
• Decompress images: tar -xzvf images.tgz.
• Loading the dataset and process the sample according to your need.

import json

with open("arxivqa.jsonl", 'r') as fr:
  arxiv_qa = [ json.loads(line.strip()) for line in fr]

sample = arxiv_qa[0]
print(sample["image"]) # image file 

Dataset details

Dataset type: ArxivQA is a set of GPT4V-generated VQA samples based on figures from Arxiv Papers.

Papers or resources for more information: https://mm-arxiv.github.io/

License: CC-BY-SA-4.0; and it should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use

Intended use:

Primary intended uses: The primary use of ArxivQA is research on large multimodal models.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.