Abstract: We consider the problem of evaluating distinct multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes.

We introduce the Berkeley Function Leaderboard (BFCL), the first comprehensive and executable function call evaluation dedicated to assessing Large Language Models' (LLMs) ability to invoke functions.

The implementation for remote data sources sends multiple requests to update the order index of every dropped row. If your back-end supports batch updates, you can simultaneously send all data updates ...