Upload 2 files

ParamBench.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b354c063220b0154200270919e50f412ae9c334a226e288f442e8f041e7e3e5c
+size 4747403

README.md

@@ -0,0 +1,125 @@
+# ParamBench: A Graduate-Level Benchmark for Evaluating LLM Understanding on Indic Subjects
+
+<div align="center">
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![arXiv](https://img.shields.io/badge/arXiv-2508.16185-f9f107.svg)](https://arxiv.org/pdf/2508.16185)
+</div>
+
+## 📋 Overview
+
+ParamBench is a comprehensive graduate-level benchmark in Hindi designed to evaluate Large Language Models (LLMs) on their understanding of Indic subjects. The benchmark contains **17,275 multiple-choice questions** across **21 subjects**, covering a wide range of topics from Indian competitive examinations.
+
+This benchmark is specifically designed to:
+- Assess LLM performance on culturally and linguistically diverse content
+- Evaluate understanding of India-specific knowledge domains
+- Support the development of more culturally aware AI systems
+
+## 🎯 Key Features
+
+- **17,275 Questions**: Extensive collection of graduate-level MCQs in Hindi
+- **21 Subjects**: Comprehensive coverage of diverse academic domains
+- **Standardized Format**: Consistent question structure for reliable evaluation
+- **Automated Evaluation**: Scripts for benchmarking and analysis
+- **Detailed Metrics**: Subject-wise and question-type-wise performance analysis
+
+## 📊 Dataset Structure
+
+### Question Format
+Each question in the dataset includes:
+- `unique_question_id`: Unique identifier for each question
+- `question_text`: The question text
+- `option_a`, `option_b`, `option_c`, `option_d`: Four multiple choice options
+- `correct_answer`: The correct option (A, B, C, or D)
+- `subject`: Subject category
+- `exam_name`: Source examination
+- `paper_number`: Paper/section identifier
+- `question_type`: Type of question (MCQ, Blank-filling, assertion/reasoning, etc.)
+
+### Subject Distribution
+The benchmark covers 21 subjects including but not limited to:
+- Music
+- History
+- Drama and Theatre
+- Economics
+- Anthropology
+- Current Affairs
+- Indian Culture
+- And more...
+
+<img width="682" height="395" alt="image" src="https://github.com/user-attachments/assets/65a7350f-26c1-46de-9c3a-d2828296ddca" />
+
+
+## 🏗️ Repository Structure
+
+```
+ParamBench/
+├── data/
+│   └── full-data.csv          # Main dataset file
+├── checkpoints/                    # Model evaluation checkpoints
+├── results/                        # Analysis results and visualizations
+├── benchmark_script.py             # Main benchmarking script
+├── analysis_models.py              # Analysis and visualization script
+├── requirements.txt                # Python dependencies
+└── README.md                       # This file
+```
+
+## 🚀 Quick Start
+
+### Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+### Basic Requirements
+- Python 3.8+
+- PyTorch 2.0+
+- Transformers 4.45+
+- Pandas
+- NumPy
+- Plotly (for visualization)
+
+### Running Benchmarks
+
+1. **Clone the repository**
+```bash
+git clone https://github.com/yourusername/ParamBench.git
+cd ParamBench
+```
+
+2. **Run the benchmark**
+```bash
+python benchmark_script.py
+```
+
+### Configuration Options
+
+The benchmark script supports various configuration options:
+
+```python
+# In benchmark_script.py
+group_to_run = "small"  # Options: "small", "medium", "large", or "all"
+batch_size = 16         # Adjust based on GPU memory
+```
+
+
+## 📊 Running Analysis
+
+After running benchmarks, generate comprehensive analysis reports:
+
+```bash
+python analysis_models.py
+```
+
+This will generate:
+- Model performance summary CSV
+- Subject-wise accuracy charts
+- Question type analysis
+- Combined report with all metrics
+
+## 🔗 Links
+
+- [Paper](https://arxiv.org/abs/2508.16185)
+
+---