8 citations · 8 across the 15 of their papers we have counts for
17 papers
VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs
Haoyu Guo, Yuan Feng, Junlin Lv +3
Scaling Multimodal Large Language Models (MLLMs) to long-form video understanding is bottlenecked by the explosion of visual tokens, which saturates context windows and incurs proh…
LibraSpec: Dynamic Diffusion-Based Speculative Decoding via Marginal-Gain-Driven Optimization
Zexun Lin, Yuan Feng, Junlin Lv +2
Speculative decoding accelerates large language model inference by drafting multiple tokens for parallel verification, with efficiency critically determined by the speculative leng…
KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering
Zhiyang Li, Ao Ke, Yukun Cao +1
Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insufficient fine-grained visual perce…
Taming the Fragility of KV Cache Eviction in LLM Inference
Yuan Feng, Haoyu Guo, JunLin Lv +2
Large language models have revolutionized natural language processing, yet their deployment remains hampered by the substantial memory and runtime overhead of the transformer's Key…
Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams
Yuan Feng, Yukun Cao, Hairu Wang +2
Sketches, probabilistic structures for estimating item frequencies in infinite data streams with limited space, are widely used across various domains. Recent studies have shifted…
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
Hairu Wang, Yuan Feng, Yukun Cao +2
Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval…
SDD-4DGS: Static-Dynamic Aware Decoupling in Gaussian Splatting for 4D Scene Reconstruction
Dai Sun, Huhao Guan, Kun Zhang +2
Dynamic and static components in scenes often exhibit distinct properties, yet most 4D reconstruction methods treat them indiscriminately, leading to suboptimal performance in both…
Path Pooling: Training-Free Structure Enhancement for Efficient Knowledge Graph Retrieval-Augmented Generation
Hairu Wang, Yuan Feng, Xike Xie +1
Although Large Language Models achieve strong success in many tasks, they still suffer from hallucinations and knowledge deficiencies in real-world applications. Many knowledge gra…
CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective
Yuan Feng, Junlin Lv, Haoyu Guo +3
Large language models have revolutionized natural language processing but face significant challenges of high storage and runtime costs, due to the transformer architecture's relia…
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs
Zengyi Gao, Yukun Cao, Hairu Wang +4
To mitigate the hallucination and knowledge deficiency in large language models (LLMs), Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) has shown promising potentia…
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…
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning
Yukun Cao, Xike Xie, Kexin Huang
Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user i…