5 papers · 1 filter
A Mathematical Theory of Top- Sparse Attention via Total Variation Distance
Georgios Tzachristas, Lei Deng, Ioannis Tzachristas +2
We develop a unified mathematical framework for certified Top- attention truncation that quantifies approximation error at both the distribution and output levels. For a single…
AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM
Haoyu Huang, Hong Ting Tsang, Jiaxin Bai +3
Retrieval-augmented generation (RAG) has shown some success in augmenting large language models (LLMs) with external knowledge. However, as a non-parametric knowledge integration p…
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
CentroidKV: Efficient Long-Context LLM Inference via KV Cache Clustering
Jie Hu, Shengnan Wang, Yutong He +8
Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for…
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
Jiaxin Bai, Wei Fan, Qi Hu +17
We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models t…