145 citations · 145 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
Li Sun, Zhenhao Huang, Ming Zhang +1
Message Passing Neural Networks (MPNNs) is the building block of graph foundation models, but fundamentally suffer from oversmoothing and oversquashing. There has recently been a s…
cs.LG2025
Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data
Chu-Cheng Lin, Daiyi Peng, Yifeng Lu +2
Reliably composing Large Language Models (LLMs) for complex, multi-step workflows remains a significant challenge. The dominant paradigm -- optimizing discrete prompts in a pipelin…
cs.CL2024★ 145 cited
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…