SuperFC: Selective Data Utilization for a Sustainable and Effective Function-Calling Agent

Xinhua Yang, Yilun Liu, Shimin Tao, Chunguang Zhao, Weibin Meng, Minggui He, Chang Su, Rongrong Liu, Hongxia Ma, Li Zhang, Jingzhou Du, Duan Li, Jian Gao, Hao Yang, Boxing Chen, Chuanwen Li

Abstract: The function-calling agent is obtained by performing agent tuning to the large language model (LLM) on function-calling dataset. However, even state-of-the-art datasets (e.g., xlam-function-calling-60k datasets) still contain numerous misleading examples of low-quality data, wasting significant computational resources and result in an unnecessary carbon footprint. Furthermore, such inductive bad data negatively impacts the performance of the agent. In this paper, we propose a set of scoring criteria specifically tailored to evaluate function-calling data and use these criteria to develop a data filtering framework. By applying this framework to filter out low-quality data, we fine-tuned SuperFC, which demonstrates substantial improvements in both sustainability and performance. The SuperFC-7B training process reduced training time from 455 minutes to 85 minutes, resulting in a 80.02% reduction in carbon footprint. Simultaneously, fine-tuning on high-quality data subsets led to performance improvements of up to 3.68%. Additionally, we provide an in-depth analysis of the causes behind the low quality of synthetic function-calling data, offering valuable insights for future data synthesis in this domain. We have also released a high-quality function-calling dataset, available at: https://github.com/Zire-Young/SuperFC

External IDs:dblp:conf/ijcnn/YangLTZMHSLMZDLGYCL25