Search Results for author:
Found 8 papers, 3 papers with code
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples
Most of the existing defense methods improve the adversarial robustness by making the models adapt to the training set augmented with some adversarial examples.
Decoding Continuous Character-based Language from Non-invasive Brain Recordings
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge.
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.
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions.
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).
