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Reja1
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jee-neet-benchmark

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Visual Question Answering
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Question Answering
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JEE/NEET LLM Benchmark Dataset

License: MIT

Dataset Description

This repository contains a benchmark dataset designed for evaluating the capabilities of Large Language Models (LLMs) on questions from major Indian competitive examinations:

  • JEE (Main & Advanced): Joint Entrance Examination for engineering.
  • NEET: National Eligibility cum Entrance Test for medical fields.

The questions are presented in image format (.png) as they appear in the original papers. The dataset includes metadata linking each image to its corresponding exam details (name, year, subject, question type), and correct answer(s). The benchmark framework supports various question types including Single Correct MCQs, Multiple Correct MCQs (with partial marking for JEE Advanced), and Integer type questions.

Current Data:

  • NEET 2024 (Code T3): 200 questions across Physics, Chemistry, Botany, and Zoology
  • NEET 2025 (Code 45): 180 questions across Physics, Chemistry, Botany, and Zoology
  • JEE Advanced 2024 (Paper 1 & 2): 102 questions across Physics, Chemistry, and Mathematics
  • Total: 380+ questions with comprehensive metadata

Key Features

  • 🖼️ Multimodal Reasoning: Uses images of questions directly, testing the multimodal reasoning capability of models
  • 📊 Exam-Specific Scoring: Implements authentic scoring rules for different exams and question types, including partial marking for JEE Advanced
  • 🔄 Robust API Handling: Built-in retry mechanism and re-prompting for failed API calls or parsing errors
  • 🎯 Flexible Filtering: Filter by exam name, year, or specific question IDs for targeted evaluation
  • 📈 Comprehensive Results: Generates detailed JSON and human-readable Markdown summaries with section-wise breakdowns
  • 🔧 Easy Configuration: Simple YAML-based configuration for models and parameters

How to Use

Using datasets Library

The dataset is designed to be loaded using the Hugging Face datasets library:

from datasets import load_dataset

# Load the evaluation split
dataset = load_dataset("Reja1/jee-neet-benchmark", split='test') # Replace with your HF repo name

# Example: Access the first question
example = dataset[0]
image = example["image"]
question_id = example["question_id"]
subject = example["subject"]
correct_answers = example["correct_answer"]

print(f"Question ID: {question_id}")
print(f"Subject: {subject}")
print(f"Correct Answer(s): {correct_answers}")
# Display the image (requires Pillow)
# image.show()

Manual Usage (Benchmark Scripts)

This repository contains scripts to run the benchmark evaluation directly:

  1. Clone the repository:

    # Replace with your actual repository URL
git clone https://github.com/your-username/jee-neet-benchmark
cd jee-neet-benchmark
# Ensure Git LFS is installed and pull large files if necessary
# git lfs pull

  2. Install dependencies:

    # It's recommended to use a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

  3. Configure API Key:

    • Create a file named .env in the root directory of the project.
    • Add your OpenRouter API key to this file:
      OPENROUTER_API_KEY=your_actual_openrouter_api_key_here
    • Important: The .gitignore file is already configured to prevent committing the .env file. Never commit your API keys directly.

  4. Configure Models:

    • Edit the configs/benchmark_config.yaml file.
    • Modify the openrouter_models list to include the specific model identifiers you want to evaluate:
      openrouter_models:
  - "google/gemini-2.5-pro-preview-03-25"
  - "openai/gpt-4o"
  - "anthropic/claude-3-5-sonnet-20241022"
    • Ensure these models support vision input on OpenRouter.
    • You can also adjust other parameters like max_tokens and request_timeout if needed.

  5. Run the benchmark:

    Basic usage (run all available models on all questions):

    python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "google/gemini-2.5-pro-preview-03-25"

    Filter by exam and year:

    # Run only NEET 2024 questions
python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "openai/gpt-4o" --exam_name NEET --exam_year 2024

# Run only JEE Advanced 2024 questions
python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "anthropic/claude-3-5-sonnet-20241022" --exam_name JEE_ADVANCED --exam_year 2024

    Run specific questions:

    # Run specific question IDs
python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "google/gemini-2.5-pro-preview-03-25" --question_ids "N24T3001,N24T3002,JA24P1M01"

    Custom output directory:

    python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "openai/gpt-4o" --output_dir my_custom_results

    Available filtering options:

    • --exam_name: Choose from NEET, JEE_MAIN, JEE_ADVANCED, or all (default)
    • --exam_year: Choose from available years (2024, 2025, etc.) or all (default)
    • --question_ids: Comma-separated list of specific question IDs to evaluate (e.g., "N24T3001,JA24P1M01")

  6. Check Results:

    • Results for each model run will be saved in timestamped subdirectories within the results/ folder.
    • Each run's folder (e.g., results/google_gemini-2.5-pro-preview-03-25_NEET_2024_20250524_141230/) contains:
      • predictions.jsonl: Detailed results for each question including:
        • Model predictions and ground truth
        • Raw LLM responses
        • Evaluation status and marks awarded
        • API call success/failure information
      • summary.json: Overall scores and statistics in JSON format
      • summary.md: Human-readable Markdown summary with:
        • Overall exam scores
        • Section-wise breakdown (by subject)
        • Detailed statistics on correct/incorrect/skipped questions

Scoring System

The benchmark implements authentic scoring systems for each exam type:

NEET Scoring

  • Single Correct MCQ: +4 for correct, -1 for incorrect, 0 for skipped

JEE Main Scoring

  • Single Correct MCQ: +4 for correct, -1 for incorrect, 0 for skipped
  • Integer Type: +4 for correct, 0 for incorrect, 0 for skipped

JEE Advanced Scoring

  • Single Correct MCQ: +3 for correct, -1 for incorrect, 0 for skipped
  • Multiple Correct MCQ: Complex partial marking system:
    • +4 for all correct options selected
    • +3 for 3 out of 4 correct options (when 4 are correct)
    • +2 for 2 out of 3+ correct options
    • +1 for 1 out of 2+ correct options
    • -2 for any incorrect option selected
    • 0 for skipped
  • Integer Type: +4 for correct, 0 for incorrect, 0 for skipped

Advanced Features

Retry Mechanism

  • Automatic retry for failed API calls (up to 3 attempts with exponential backoff)
  • Separate retry pass for questions that failed initially
  • Comprehensive error tracking and reporting

Re-prompting System

  • If initial response parsing fails, the system automatically re-prompts the model
  • Uses the previous response to ask for properly formatted answers
  • Adapts prompts based on question type (MCQ vs Integer)

Comprehensive Evaluation

  • Tracks multiple metrics: correct answers, partial credit, skipped questions, API failures
  • Section-wise breakdown by subject
  • Detailed logging with color-coded progress indicators

Dataset Structure

  • data/metadata.jsonl: Contains metadata for each question image with fields:

    • image_path: Path to the question image
    • question_id: Unique identifier (e.g., "N24T3001")
    • exam_name: Exam type ("NEET", "JEE_MAIN", "JEE_ADVANCED")
    • exam_year: Year of the exam (integer)
    • exam_code: Paper/session code (e.g., "T3", "P1")
    • subject: Subject name (e.g., "Physics", "Chemistry", "Mathematics")
    • question_type: Question format ("MCQ_SINGLE_CORRECT", "MCQ_MULTIPLE_CORRECT", "INTEGER")
    • correct_answer: List of correct answer strings (e.g., ["A"], ["B", "C"], ["42"])

  • images/: Contains subdirectories for each exam set:

    • images/NEET_2024_T3/: NEET 2024 question images
    • images/NEET_2025_45/: NEET 2025 question images
    • images/JEE_ADVANCE_2024/: JEE Advanced 2024 question images

  • src/: Python source code for the benchmark system:

    • benchmark_runner.py: Main benchmark execution script
    • llm_interface.py: OpenRouter API interface with retry logic
    • evaluation.py: Scoring and evaluation functions
    • prompts.py: LLM prompts for different question types
    • utils.py: Utility functions for parsing and configuration

  • configs/: Configuration files:

    • benchmark_config.yaml: Model selection and API parameters

  • results/: Directory where benchmark results are stored (timestamped subdirectories)

  • jee-neet-benchmark.py: Hugging Face datasets loading script

Data Fields

The dataset contains the following fields (accessible via datasets):

  • image: The question image (datasets.Image)
  • question_id: Unique identifier for the question (string)
  • exam_name: Name of the exam (e.g., "NEET", "JEE_ADVANCED") (string)
  • exam_year: Year of the exam (int)
  • exam_code: Paper/session code (e.g., "T3", "P1") (string)
  • subject: Subject (e.g., "Physics", "Chemistry", "Mathematics") (string)
  • question_type: Type of question (e.g., "MCQ_SINGLE_CORRECT", "INTEGER") (string)
  • correct_answer: List containing the correct answer strings.
    • For MCQs, these are option identifiers (e.g., ["1"], ["A"], ["B", "C"]). The LLM should output the identifier as it appears in the question.
    • For INTEGER type, this is the numerical answer as a string (e.g., ["42"], ["12.75"]). The LLM should output the number.
    • For some MCQ_SINGLE_CORRECT questions, multiple answers in this list are considered correct if the LLM prediction matches any one of them. (list of strings)

LLM Answer Format

The LLM is expected to return its answer enclosed in <answer> tags. For example:

  • MCQ Single Correct (Option A): <answer>A</answer>
  • MCQ Single Correct (Option 2): <answer>2</answer>
  • MCQ Multiple Correct (Options B and D): <answer>B,D</answer>
  • Integer Answer: <answer>42</answer>
  • Decimal Answer: <answer>12.75</answer>
  • Skipped Question: `SKIP

The system parses these formats. Prompts are designed to guide the LLM accordingly.

Troubleshooting

Common Issues

API Key Issues:

  • Ensure your .env file is in the root directory
  • Verify your OpenRouter API key is valid and has sufficient credits
  • Check that the key has access to vision-capable models

Model Not Found:

  • Verify the model identifier exists on OpenRouter
  • Ensure the model supports vision input
  • Check your OpenRouter account has access to the specific model

Memory Issues:

  • Reduce max_tokens in the config file
  • Process smaller subsets using --question_ids filter
  • Use models with smaller context windows

Parsing Failures:

  • The system automatically attempts re-prompting for parsing failures
  • Check the raw responses in predictions.jsonl to debug prompt issues
  • Consider adjusting prompts in src/prompts.py for specific models

Current Limitations

  • Dataset Size: While comprehensive, the dataset could benefit from more JEE Main questions and additional years
  • Language Support: Currently only supports English questions
  • Model Dependencies: Requires models with vision capabilities available through OpenRouter

Citation

If you use this dataset or benchmark code, please cite:

@misc{rejaullah_2025_jeeneetbenchmark,
  title={JEE/NEET LLM Benchmark},
  author={Md Rejaullah},
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/Reja1/jee-neet-benchmark}},
}

Contact

For questions, suggestions, or collaboration, feel free to reach out:

License

This dataset and associated code are licensed under the MIT License.

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