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Found 8 papers, 1 papers with code
Decoding Continuous Character-based Language from Non-invasive Brain Recordings
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge.
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters.
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data.
Open the Pandora's Box of LLMs: Jailbreaking LLMs through Representation Engineering
To overcome these limitations, we propose a novel jailbreaking approach, named Jailbreaking LLMs through Representation Engineering (JRE).
Aligning Large Language Models with Human Preferences through Representation Engineering
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness.
Tailoring Personality Traits in Large Language Models via Unsupervisedly-Built Personalized Lexicons
Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs.
SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network
Spiking neural networks (SNNs) have demonstrated the capability to achieve comparable performance to deep neural networks (DNNs) in both visual and linguistic domains while offering the advantages of improved energy efficiency and adherence to biological plausibility.
SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way.
