With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

Generative large language models (LLMs), eg, ChatGPT, have demonstrated remarkable
proficiency across several NLP tasks such as machine translation, question answering, text …

State-of-the-art parametric and non-parametric style transfer approaches are prone to either
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Abstract Knowledge distillation (KD) is the preliminary step for training non-autoregressive
translation (NAT) models, which eases the training of NAT models at the cost of losing …

Adaptive training approaches, widely used in sequence-to-sequence models, commonly
reweigh the losses of different target tokens based on priors, eg word frequency. However …

Abstract Non-autoregressive Transformers (NATs) significantly reduce the decoding latency
by generating all tokens in parallel. However, such independent predictions prevent NATs …

We propose a new training objective named order-agnostic cross entropy (OaXE) for fully
non-autoregressive translation (NAT) models. OaXE improves the standard cross-entropy …

The intersection of quantum computing and artificial intelligence (AI) within the cloud
environment represents a paradigm shift in the capabilities of computational technologies …

Recent research shows that pre-trained language models (PLMs) suffer from" prompt bias"
in factual knowledge extraction, ie, prompts tend to introduce biases toward specific labels …