8 citations · 8 across the 15 of their papers we have counts for
6 papers · 1 filter
LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration
Yukun Cao, Zengyi Gao, Zhiyang Li +3
GraphRAG integrates (knowledge) graphs with large language models (LLMs) to improve reasoning accuracy and contextual relevance. Despite its promising applications and strong relev…
Prototype-based Optimal Transport for Out-of-Distribution Detection
Ao Ke, Wenlong Chen, Chuanwen Feng +4
Detecting Out-of-Distribution (OOD) inputs is crucial for improving the reliability of deep neural networks in the real-world deployment. In this paper, inspired by the inherent di…
CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs
Junlin Lv, Yuan Feng, Xike Xie +3
Large language models have achieved notable success across various domains, yet efficient inference is still limited by the quadratic computation complexity of the attention mechan…
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding
Yukun Cao, Shuo Han, Zengyi Gao +3
Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph de…
Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference
Yuan Feng, Junlin Lv, Yukun Cao +2
Large Language Models have excelled in various domains but face efficiency challenges due to the growing Key-Value (KV) cache required for long-sequence inference. Recent efforts a…
Detecting Out-of-Distribution Samples via Conditional Distribution Entropy with Optimal Transport
Chuanwen Feng, Wenlong Chen, Ao Ke +3
When deploying a trained machine learning model in the real world, it is inevitable to receive inputs from out-of-distribution (OOD) sources. For instance, in continual learning se…