LLM Leaderboard Explorer

An interactive dashboard for exploring and visualizing merged data from LLM leaderboards, built with Gradio. Check it out our deployed HuggingFace Space: Link

📊 Overview

This application provides an interactive interface to view, filter, and compare Large Language Models (LLMs) based on aggregated data from prominent leaderboard sources:

• LiveBench: Features performance metrics like Global Average, Reasoning, Coding, Mathematics, Data Analysis, Language, and Instruction Following scores.
• LMSYS Chatbot Arena: Includes community-based Elo ratings (Arena Score), rankings, and voting data.

The dashboard allows users to easily navigate and compare models across various metrics and categories.

✨ Features

• Interactive Data Tables: View LLM data organized into tabs:
  • Performance Metrics: Core benchmark scores from LiveBench.
  • Model Details: Information like Organization, License, Knowledge Cutoff, and links.
  • Community Stats: Data from the Chatbot Arena Leaderboard (Ranks, Score, Votes).
  • Model Mapping: Shows the unified model name alongside original names from LiveBench and Arena.
• Filtering: Dynamically filter the displayed models by:
  • Search term (searches Model Name and Organization).
  • Organization.
  • Minimum Global Average score.
• Detailed Model Card: Click on any row in the data tables to view a comprehensive card summarizing all metrics for that specific model.
• Visualizations Tab:
  • Bar Chart: Compare the top 15 models based on a user-selected metric (e.g., Global Average, Arena Score, Coding Average).
  • Radar Chart: Select multiple models (up to 5) to compare their performance profile across key metrics (Reasoning, Coding, Math, Data Analysis, Language, IF Average, and scaled Arena Score).

💾 Data

The application uses a pre-merged CSV file (data/merged_leaderboards.csv) containing data aggregated from the sources mentioned above.

🚀 Getting Started

Prerequisites

• Python 3.9+
• pip (Python package installer)

Installation

1. Clone the repository (Optional):

   # If you have the code in a git repository
   git clone <your-repo-url>
   cd <your-repo-directory>

   If you just have the files, navigate to the project directory in your terminal.

2. Install Dependencies: Create a requirements.txt file with the following content:

   gradio==4.9.0
   pandas
   plotly
   numpy
   

   Then, install the requirements:

   pip install -r requirements.txt

Running the Application

To run the application locally:

python app.py

The application will typically be available at http://127.0.0.1:7860 in your web browser.

📁 Project Structure

GTLLMZoo2
├─ app.py                  # Main Gradio application entry point
├─ requirements.txt        # Python dependencies
├─ data
│  └─ merged_leaderboards.csv # Merged leaderboard data
└─ src
   ├─ data_processing.py  # Data loading and filtering logic
   └─ ui.py               # Gradio UI definition and logic

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request if you have improvements or bug fixes.

📄 License

MIT License