Reja1/jee-neet-benchmark

JEE/NEET LLM Benchmark Dataset

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
• JEE Advanced 2025 (Paper 1 & 2): 96 questions across Physics, Chemistry, and Mathematics
• Total: 578 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 hosted on the Hugging Face Hub and can be loaded directly:

from datasets import load_dataset
import json

# Load the evaluation split
dataset = load_dataset("Reja1/jee-neet-benchmark", split='test')

# Example: Access the first question
example = dataset[0]
image = example["image"]
question_id = example["question_id"]
subject = example["subject"]
correct_answers = json.loads(example["correct_answer"])  # Parse JSON string

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:

   git clone https://huggingface.co/datasets/Reja1/jee-neet-benchmark
   cd jee-neet-benchmark
   # Ensure Git LFS is installed and pull large files
   git lfs pull
   

2. Install dependencies:

   uv sync
   

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"
       - "anthropic/claude-sonnet-4"
       - "openai/o3"
     

   • 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):

   uv run 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
   uv run python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "openai/o3" --exam_name NEET --exam_year 2024
   
   # Run only JEE Advanced 2025 questions
   uv run python src/benchmark_runner.py --config configs/benchmark_config.yaml --model "anthropic/claude-sonnet-4" --exam_name JEE_ADVANCED --exam_year 2025
   

   Run specific questions:

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

   Resume an interrupted run:

   # Resume from an existing results directory (skips already-completed questions)
   uv run python src/benchmark_runner.py --model "google/gemini-2.5-pro-preview-03-25" --resume results/google_gemini-2.5-pro-preview-03-25_NEET_2024_20250524_141230
   

   Custom output directory:

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

   Available 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")
   • --resume: Path to an existing results directory to resume an interrupted run

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: Raw API responses for each question including:
       • Raw LLM responses
       • API call success/failure information
       • Parse success status and errors
     • summary.jsonl: Per-question scored results including:
       • Predicted answers and ground truth
       • Evaluation status and marks awarded
     • summary.md: Human-readable Markdown summary with:
       • Overall exam scores
       • Question type breakdown
       • 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/API failure

JEE Main Scoring

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

JEE Advanced Scoring

• Single Correct MCQ: +3 for correct, -1 for incorrect, 0 for skipped/API failure
• Multiple Correct MCQ: 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/API failure
• Integer Type: +4 for correct, 0 for incorrect, 0 for skipped/API failure

Note: API failures and parse failures are scored as 0 (no penalty) since they do not represent a deliberate wrong choice.

Advanced Features

Retry Mechanism

• Automatic retry for failed API calls (up to 3 attempts with exponential backoff)
• Retries on HTTP 429 (rate limit), 500, 502, 503, 504 status codes
• Separate retry pass for questions that failed initially
• Comprehensive error tracking and reporting

Resume Capability

• Resume interrupted benchmark runs with --resume <results_dir>
• Reads existing summary.jsonl to identify completed questions and skips them
• Appends new results to the same output files

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
• Shows only relevant format examples based on question type (MCQ single, MCQ multiple, or integer)

Comprehensive Evaluation

• Tracks multiple metrics: correct answers, partial credit, skipped questions, API failures
• Section-wise breakdown by subject
• Color-coded progress indicators in terminal output

Dataset Structure

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

  • file_name: Path to the question image (relative to repo root)
  • question_id: Unique identifier (e.g., "N24T3001")
  • exam_name: Exam type ("NEET", "JEE_MAIN", "JEE_ADVANCED")
  • exam_year: Year of the exam (integer)
  • subject: Subject name (e.g., "Physics", "Chemistry", "Mathematics")
  • question_type: Question format ("MCQ_SINGLE_CORRECT", "MCQ_MULTIPLE_CORRECT", "INTEGER")
  • correct_answer: JSON-serialized string of correct answers (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_ADVANCED_2024/: JEE Advanced 2024 question images
  • images/JEE_ADVANCED_2025/: JEE Advanced 2025 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)

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)
• subject: Subject (e.g., "Physics", "Chemistry", "Mathematics") (string)
• question_type: Type of question (e.g., "MCQ_SINGLE_CORRECT", "INTEGER") (string)
• correct_answer: JSON-serialized string containing the correct answer(s). Use json.loads() to parse.
  • 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 the list are considered correct if the LLM prediction matches any one of them.

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:

• X (Twitter): https://x.com/RejaullahmdMd

License

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