2 citations · 6 across the 13 of their papers we have counts for
14 papers
ReFilter: Improving Robustness of Retrieval-Augmented Generation via Gated Filter
Yixin Chen, Ying Xiong, Shangyu Wu +3
Retrieval-augmented generation (RAG) has become a dominant paradigm for grounding large language models (LLMs) with external evidence in knowledge-intensive question answering. A c…
ClawMobile: Rethinking Smartphone-Native Agentic Systems
Hongchao Du, Shangyu Wu, Qiao Li +4
Smartphones represent a uniquely challenging environment for agentic systems. Unlike cloud or desktop settings, mobile devices combine constrained execution contexts, fragmented co…
ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Management
Jing Zou, Shangyu Wu, Hancong Duan +2
Efficiently serving Large Language Models (LLMs) with persistent Prefix Key-Value (KV) Cache is critical for applications like conversational search and multi-turn dialogue. Servin…
Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Yixin Chen, Ying Xiong, Shangyu Wu +4
Tool-augmented LLMs invoke external functions to extend their capabilities, but errors in the invocation decision, such as calling a tool when none is needed or omitting a needed c…
AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving
Lianming Huang, Haibo Hu, Yufei Cui +4
With the rapid advancement of autonomous driving, deploying Vision-Language Models (VLMs) to enhance perception and decision-making has become increasingly common. However, the rea…
FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference
Hongchao Du, Shangyu Wu, Arina Kharlamova +2
Large Language Models (LLMs) face challenges for on-device inference due to high memory demands. Traditional methods to reduce memory usage often compromise performance and lack ad…
EvoP: Robust LLM Inference via Evolutionary Pruning
Shangyu Wu, Hongchao Du, Ying Xiong +4
Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks, but their massive size and computational demands hinder their deployment in reso…
AATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization
Junhui He, Junna Xing, Nan Wang +6
Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. Retrieval-based…
GeneQuery: A General QA-based Framework for Spatial Gene Expression Predictions from Histology Images
Ying Xiong, Linjing Liu, Yufei Cui +4
Gene expression profiling provides profound insights into molecular mechanisms, but its time-consuming and costly nature often presents significant challenges. In contrast, whole-s…
CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification
Junhui He, Shangyu Wu, Weidong Wen +2
Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory requirements. Activation sparsificat…
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Shangyu Wu, Ying Xiong, Yufei Cui +8
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still…
RAEE: A Robust Retrieval-Augmented Early Exit Framework for Efficient Inference
Lianming Huang, Shangyu Wu, Yufei Cui +6
Deploying large language model inference remains challenging due to their high computational overhead. Early exit optimizes model inference by adaptively reducing the number of inf…
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
Shangyu Wu, Ying Xiong, Yufei Cui +4
Retrieval-based augmentations (RA) incorporating knowledge from an external database into language models have greatly succeeded in various knowledge-intensive (KI) tasks. However,…
NFL: Robust Learned Index via Distribution Transformation
Shangyu Wu, Yufei Cui, Jinghuan Yu +3
Recent works on learned index open a new direction for the indexing field. The key insight of the learned index is to approximate the mapping between keys and positions with piece-…