dataset_info:
features:
- name: question_id
dtype: string
- name: image
dtype: image
- name: subject
dtype: string
- name: question_type
dtype: string
- name: year
dtype: string
- name: paper
dtype: string
- name: language
dtype: string
- name: answer
dtype: string
- name: answer_sources
dtype: string
- name: requires_image
dtype: bool
splits:
- name: train
num_bytes: 101253285.86
num_examples: 1460
download_size: 97675003
dataset_size: 101253285.86
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- image-text-to-text
- question-answering
license: mit
language:
- en
- hi
tags:
- multimodal
- vlm
- scientific-reasoning
- benchmark
- education
mmJEE-Eval: A Bilingual Multimodal Benchmark for Exam-Style Evaluation of Vision-Language Models
Paper: mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models Code: https://github.com/ArkaMukherjee0/mmJEE-Eval Project Page: https://mmjee-eval.github.io
Introduction
mmJEE-Eval is a multimodal and bilingual dataset for LLM evaluation comprising 1,460 challenging questions from seven years (2019-2025) of India's JEE Advanced competitive examination. We evaluate 17 state-of-the-art VLMs, finding that open models (from 7B-400B) struggle significantly (maxing at 40-50%) as compared to frontier models from Google and OpenAI (77-84%). mmJEE-Eval is significantly more challenging than the text-only JEEBench, the only other well-established dataset on JEE Advanced problems, with performance drops of 18-56% across all models. Our findings, especially metacognitive self-correction abilities, cross-lingual consistency, and human evaluation of reasoning quality, demonstrate that contemporary VLMs still show authentic scientific reasoning deficits despite strong question-solving capabilities (as evidenced by high Pass@K accuracies), establishing mmJEE-Eval as a challenging complementary benchmark that effectively discriminates between model capabilities.
Sample Usage
You can load the dataset using the Hugging Face datasets library:
from datasets import load_dataset
# Load the entire dataset
dataset = load_dataset("ArkaMukherjee/mmJEE-Eval")
# Access the training split
train_dataset = dataset["train"]
# Print an example
print(train_dataset[0])
# To run evaluation scripts, please refer to the official GitHub repository:
# https://github.com/ArkaMukherjee0/mmJEE-Eval