---
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

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**Paper:** [mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models](https://huggingface.co/papers/2511.09339)
**Code:** [https://github.com/ArkaMukherjee0/mmJEE-Eval](https://github.com/ArkaMukherjee0/mmJEE-Eval)
**Project Page:** [https://mmjee-eval.github.io](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:

```python
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
```