alucent/mirror-XLAM-7.5k-Irrelevance

xlam-irrelevance-7.5k

Overview

The xlam-irrelevance-7.5k is a specialized dataset designed to activate the ability of irrelevant function detection for large language models (LLMs).

Source and Construction

This dataset is built upon xlam-function-calling-60k dataset, from which we random sampled 7.5k instances, removed the ground truth function from the provided tool list, and relabel them as irrelevant. For more details, please refer to Hammer: Robust Function-Calling for On-Device Language Models via Function Masking and Hammer GitHub repository .

Application

This dataset is a supplement to the xLAM dataset. After integrating the data from these two parts, we trained the Hammer series of models.