作者位次 #6/8ACL 2026Poster· Natural Language Processing
G enesis F unc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling
Hao-Xiang Xu · Chong Deng · Jiaqing Liu · Wen Wang · Qian Chen · Lujia Bao · Xiangang Li · Zhen-Hua Ling
摘要
Large Language Models (LLMs) extend their capabilities through function-calling (FC), which relies on training data with high quality, diversity, and broad coverage of scenario. However, obtaining and annotating real function-calling data is challenging, while synthetic data from existing pipelin...