Awesome Foundation Model Leaderboard
The Awesome Foundation Model Leaderboard project serves as a refined collection of top-tier leaderboards, specifically for foundation models. Foundation models are large-scale, sophisticated AI models, like those used for language processing or image recognition. For those unacquainted with the concept of a leaderboard, it refers to a ranked list or table that tracks achievements and scores, commonly used to compare the performance of models in a particular domain. More details can be found in this post.
This project is part of a broader study titled "On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards." Conducted by researchers including Zhimin (Jimmy) Zhao, Abdul Ali Bangash, Filipe Roseiro Côgo, Bram Adams, and Ahmed E. Hassan from the Software Analysis and Intelligence Lab (SAIL), this study investigates how leaderboards operate and the challenges associated with them in the context of foundation models.
The project includes an extensive array of resources, such as:
- Development Tools and Evaluation Organizations: A variety of tools and organizations are linked to provide users with additional support for managing and evaluating leaderboards.
- Search Toolkit: A search toolkit is available to help users quickly navigate different leaderboards.
- Contribution and Feedback Opportunities: Users are invited to contribute to the list or address queries and suggestions via pull requests or issues on the project's GitHub repository.
- Citing the Study: If the repository proves useful, researchers encourage citations as per the provided reference format.
The leaderboards are typically added only if they meet two criteria: they must be actively maintained, and they should be related to foundation models.
Tools
The project lists a variety of tools designed to aid in the creation and management of leaderboards. These include:
- Demo Leaderboard: A demo platform for deploying leaderboards with a standardized template.
- Leaderboard Explorer: Tool for exploring diverse leaderboards.
- Open LLM Leaderboard Tools: Set of tools including renamers and scrapers for efficiently managing open LLM leaderboards.
- Progress Tracker: Visualizes the performance progress of both proprietary and open-source LLMs over time.
Challenges
The project outlines numerous platforms hosting AI challenges or competitions, ideal for those interested in testing or demonstrating AI models:
- AIcrowd & Kaggle: Popular for hosting diverse machine learning challenges across domains.
- AI Studio & Tianchi: Platforms focusing particularly on competitions in computer vision, NLP, and other data-driven tasks.
- Codabench & Eval AI: Open-source benchmarking platforms widely used in AI research for model evaluation.
Rankings
This section offers insight into various ranking categories including:
- Model, Database, and Dataset Rankings: Evaluating different aspects such as language models and databases.
- Metric and Paper Rankings: Ranking models based on metrics and academic contributions.
The "Model Ranking" further delves into categories such as text, image, and video, making it a resource-rich environment for users aiming to compare and improve their foundation models.
Overall, the Awesome Foundation Model Leaderboard stands as a pivotal project for enthusiasts and professionals within the AI domain, offering a comprehensive overview of resources, challenges, and evaluation mechanisms associated with foundation models. By participating, users can contribute to the advancement of foundational AI technology.