3 citations · 7 across the 11 of their papers we have counts for
4 papers · 1 filter
Enhancing the Capability and Robustness of Large Language Models through Reinforcement Learning-Driven Query Refinement
Xiaohua Wang, Zisu Huang, Feiran Zhang +5
The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to…
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…
Aligning Large Language Models with Human Preferences through Representation Engineering
Wenhao Liu, Xiaohua Wang, Muling Wu +7
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness…
SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation
Changze Lv, Tianlong Li, Jianhan Xu +5
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for la…