activity
20232026
most citedPromoting Data and Model Privacy in Federated Learning through Quantized LoRA

1 citations · 3 across the 13 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.AI2024

Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing

Wenhao Liu, Siyu An, Junru Lu +8

Role-Playing Agents (RPAs) have shown remarkable performance in various applications, yet they often struggle to recognize and appropriately respond to hard queries that conflict w…

cs.NE2024

Towards Biologically Plausible Computing: A Comprehensive Comparison

Changze Lv, Yufei Gu, Zhengkang Guo +16

Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the dis…

cs.LG2024★ 1 cited

Promoting Data and Model Privacy in Federated Learning through Quantized LoRA

JianHao Zhu, Changze Lv, Xiaohua Wang +7

Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for paramete…

cs.LG2024

Advancing Parameter Efficiency in Fine-tuning via Representation Editing

Muling Wu, Wenhao Liu, Xiaohua Wang +7

Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adju…

cs.CL2024

Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective

Tianlong Li, Zhenghua Wang, Wenhao Liu +6

The recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) when exposed to malicious inputs. While various defense strategies…