Introduction to LLM-Leaderboard
The LLM-Leaderboard is a collaborative project designed to serve as a central repository for tracking the performance and usability of large language models (LLMs). An LLM is considered "open" if it can be deployed locally and used for commercial endeavors. This project invites contributions and corrections from the community, ensuring that the information remains accurate and comprehensive.
Interactive Dashboard
To explore the leaderboard in a more interactive manner, users can visit:
These interactive dashboards offer a user-friendly interface for comparing and contrasting various LLMs based on multiple performance metrics.
The Leaderboard
The LLM-Leaderboard includes a detailed table showcasing a variety of language models, along with their publishers, openness for commercial use, and performance metrics across several benchmarks. Some of these key evaluation criteria include:
- Chatbot Arena Elo: A score representing the model’s performance in a competitive chatbot arena setting.
- HellaSwag Scores: Measurements presented in few-shot, zero-shot, and one-shot learning contexts. These scores indicate how well a model can answer questions accurately with varying amounts of context.
- HumanEval-Python: The "pass@1" metric indicating the model's ability to solve coding problems using Python.
- LAMBADA dataset evaluations: Zero-shot and one-shot capabilities of the models are tested using this reading comprehension dataset.
- MMLU Benchmarks: Zero-shot and few-shot evaluations in multi-task settings.
- TriviaQA Assessments: A benchmark standard for assessing knowledge on a broad range of topics in zero-shot and one-shot settings.
- WinoGrande Challenge Results: Zero-shot, one-shot, and few-shot scores evaluating the model’s capability in resolving ambiguous pronouns, assessing commonsense reasoning.
Each model is listed with links to its detailed page for further information:
- Model Name: Links to specific documentation or research papers are provided.
- Publisher: The name of the organization or team responsible for the model’s development.
- Open?: Indicates whether the model is open for local deployment and commercial use.
Notable entries in the leaderboard include models like GPT-3.5, Bloom-176b, Cerebras-GPT, and many others from prominent developers such as OpenAI, Meta AI, and Salesforce. Each model has been evaluated using diverse datasets and test scenarios to rate its proficiency, thus facilitating a comprehensive comparison across different application areas.
By compiling these elements into one accessible location, the LLM-Leaderboard acts as a vital resource for researchers, developers, and businesses looking to understand the capabilities and suitability of various LLMs for specific tasks or projects. This effort reflects a broad community's commitment to advancing AI technology by promoting transparency and enabling informed decision-making in the field.