Berkeley Function-Calling Leaderboard
Explore and compare the function-calling capabilities of large language models with real-world data on Berkeley Function-Calling Leaderboard.
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Berkeley Function-Calling Leaderboard is an online platform that evaluates large language models' ability to accurately call functions or tools. It's based on real-world data and updates regularly, offering a benchmark for comparing different models on specific programming tasks.
Who can benefit from this leaderboard?
This leaderboard is ideal for AI researchers, developers, and anyone interested in evaluating large language models' programming capabilities. It helps users choose the most suitable model for their projects based on performance, cost, and efficiency.
Example Scenarios:
Researchers use the leaderboard to compare different LLMs on specific programming tasks.
Developers select the best model for their applications using the leaderboard data.
Educational institutions may use it as a resource to showcase the latest advancements in AI technology.
Key Features:
Assesses function-calling abilities of large language models
Uses real-world data for evaluation
Regularly updated to reflect current technological advancements
Provides detailed error analysis to help understand model strengths and weaknesses
Enables comparison between models for better selection
Offers cost and latency estimates to assist with economic and efficient choices
How to Use the Leaderboard:
Visit the Berkeley Function-Calling Leaderboard website.
Check the current leaderboard to see model scores and rankings.
Click on any model to get detailed information and evaluation data.
Use the error analysis tool to understand model performance across various errors.
Review cost and latency estimates to assess economic and response time efficiency.
If needed, contact the site through provided channels to submit your own model or contribute test cases.
