8 citations · 29 across the 44 of their papers we have counts for
11 papers · 1 filter
Multi-Programming Language Sandbox for LLMs
Shihan Dou, Jiazheng Zhang, Jianxiang Zang +25
We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Lang…
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…
Searching for Best Practices in Retrieval-Augmented Generation
Xiaohua Wang, Zhenghua Wang, Xuan Gao +11
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, partic…
Spiking Convolutional Neural Networks for Text Classification
Changze Lv, Jianhan Xu, Xiaoqing Zheng
Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and i…
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…
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…
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
Changze Lv, Dongqi Han, Yansen Wang +3
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying…
Decoding Continuous Character-based Language from Non-invasive Brain Recordings
Cenyuan Zhang, Xiaoqing Zheng, Ruicheng Yin +8
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge. Previous non-invasive decoders either require multiple experiments wit…
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…
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Changze Lv, Yansen Wang, Dongqi Han +3
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying S…
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…