Berkeley Function-Calling Leaderboard

Leaderboard for evaluating the function calling ability of large language models

CommonProductProgrammingAI EvaluationProgramming

The Berkeley Function-Calling Leaderboard (BCL) is an online platform specifically designed to evaluate the accuracy of large language models (LLMs) in calling functions (or tools). The leaderboard is based on real-world data and is regularly updated, providing a benchmark for measuring and comparing the performance of different models on specific programming tasks. It is a valuable resource for developers, researchers, and anyone interested in the programming capabilities of AI.

Provides an assessment of large language model function calling capabilities
Includes an evaluation set based on real-world data
The leaderboard is updated regularly to reflect the latest technological advancements
Provides detailed error type analysis
helping users understand the strengths and weaknesses of the model
Supports model comparisons
enabling users to select the most suitable model
Provides estimates of model cost and latency to assist users in making economical and efficient choices

This product is suitable for AI researchers
developers
and technical personnel interested in the programming capabilities of large language models. It helps them understand the performance of different models in function calling tasks
choose the model that best fits their project needs
and evaluate the economy and efficiency of the model.

Researchers use the leaderboard to compare the performance of different LLMs on specific programming tasks.
Developers leverage leaderboard data to select AI models suitable for their application scenarios.
Educational institutions may use the platform as a teaching resource to demonstrate the latest advancements in AI technology.

Visit the Berkeley Function-Calling Leaderboard website.
View the current leaderboard to see the scores and rankings of the different models.
Click on a model of interest to access its detailed information and evaluation data.
Use the error type analysis tool to understand the model's performance on different error types.
Refer to the cost and latency estimates to evaluate the model's economy and response speed.

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