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20202026
most citedSafely Learning with Private Data: A Federated Learning Framework for Large Language Model

28 citations · 89 across the 30 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.SI2024

Know Your Account: Double Graph Inference-based Account De-anonymization on Ethereum

Shuyi Miao, Wangjie Qiu, Hongwei Zheng +6

The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token…

cs.CL2024

AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models

Qianchi Zhang, Hainan Zhang, Liang Pang +2

Retrieved documents containing noise will hinder RAG from detecting answer clues and make the inference process slow and expensive. Therefore, context compression is necessary to e…

physics.soc-ph2024★ 26 cited

Evolutionary dynamics in stochastic nonlinear public goods games

Wenqiang Zhu, Xin Wang, Chaoqian Wang +6

Understanding the evolution of cooperation in multiplayer games is of vital significance for natural and social systems. An important challenge is that group interactions often lea…

cs.CL2024★ 1 cited

MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models

Yujing Wang, Hainan Zhang, Liang Pang +3

In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user'…

cs.CR2024★ 28 cited

Safely Learning with Private Data: A Federated Learning Framework for Large Language Model

JiaYing Zheng, HaiNan Zhang, LingXiang Wang +3

Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in m…