32 citations · 32 across the 2 of their papers we have counts for
2 papers
cs.CL2026
VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
Ying Nie, Kai Han, Hongguang Li +5
The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such a…
cs.CL2025★ 32 cited
Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
Yuhan Ji, Song Gao, Ying Nie +2
Applying AI foundation models directly to geospatial datasets remains challenging due to their limited ability to represent and reason with geographical entities, specifically vect…