作者位次 #8/10ACL 2026Findings· Findings of ACL
U ni G e M : Unifying Data Selection and Mixing via Geometric Exploration and Mining
Changhao Wang · Yunfeiyu · Xinhao Yao · Jiaolong Yang · Lu Yu · Junpeng Fang · Chaobo Li · Riccardo Cantoro · Qing Cui · JUN ZHOU
摘要
The scaling of Large Language Models (LLMs) is increasingly limited by data quality. Most methods handle data mixing and sample selection separately, which can break the structure in code corpora. We introduce UniGeM, a framework that unifies mixing and selection by treating data curation as a ma...