Run benchmarks on prediction models
Merge machine learning models using a YAML configuration file
Compare and rank LLMs using benchmark scores
Teach, test, evaluate language models with MTEB Arena
Evaluate AI-generated results for accuracy
Browse and filter machine learning models by category and modality
View and submit LLM benchmark evaluations
Evaluate reward models for math reasoning
Open Persian LLM Leaderboard
Load AI models and prepare your space
Search for model performance across languages and benchmarks
Track, rank and evaluate open LLMs and chatbots
GIFT-Eval: A Benchmark for General Time Series Forecasting
The LLM Forecasting Leaderboard is a comprehensive tool designed for evaluating and comparing the performance of large language models (LLMs) in forecasting tasks. It provides a platform to benchmark prediction models, enabling users to assess their accuracy, reliability, and effectiveness in generating future predictions based on historical data. This leaderboard is particularly useful for researchers, data scientists, and professionals seeking to identify top-performing models for forecasting applications.
• Model Benchmarking: Compare the performance of different LLMs on various forecasting tasks. • Detailed Performance Metrics: Access metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared to evaluate model accuracy. • Customizable Forecasts: Define specific forecasting parameters to suit different use cases. • Historical Data Support: Leverage historical data to train and test models for future predictions. • Real-Time Updates: Stay informed with the latest model performance data and leaderboard rankings. • User-Friendly Interface: Easily navigate and interpret forecasting results through an intuitive dashboard. • Collaboration Tools: Share insights and compare results with colleagues or peers.
What types of forecasting tasks does the LLM Forecasting Leaderboard support?
The leaderboard supports a variety of forecasting tasks, including time series prediction, demand forecasting, and financial prediction, among others.
How often is the leaderboard updated?
The leaderboard is updated regularly to reflect the latest model performance data and new model releases.
Can I submit my own model for benchmarking?
Yes, you can submit your own model for evaluation. Please refer to the platform's documentation for submission guidelines and requirements.