1 citations · 1 across the 9 of their papers we have counts for
5 papers · 1 filter
CEDAR: Error-Bounded Residual Routing for Efficient Long-Context Attention
Siyu Li, Dong Wang, Jie Zhou +3
Post-hoc sparse attention accelerates long-context prefill by routing each query to a small set of token-level interactions. Hard selection, however, assigns zero probability to ev…
UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing
Xinlong Zhao, Dongsheng Liu, Hengyu Zhao +9
As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Models (LLMs) now depends less on d…
MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling
MiniCPM Team, Wenhao An, Yingfa Chen +44
The evolution of large language models (LLMs) towards applications with ultra-long contexts faces challenges posed by the high computational and memory costs of the Transformer arc…
FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
Yingli Shen, Wen Lai, Jie Zhou +7
Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages. Existing resources impose a tr…
MiniCPM4: Ultra-Efficient LLMs on End Devices
MiniCPM Team, Chaojun Xiao, Yuxuan Li +80
This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…