AI has provided us with the ability to automate tasks, extract information from vast amounts of
data, and synthesize media that is nearly indistinguishable from the real thing. However …

The stylistic properties of text have intrigued computational linguistics researchers in recent
years. Specifically, researchers have investigated the text style transfer task (TST), which …

In everyday conversations, humans can take on different roles and adapt their vocabulary to
their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles …

Recent advances in large language models (LLMs) and the intensifying popularity of
ChatGPT-like applications have blurred the boundary of high-quality text generation …

Adversarial attacks and backdoor attacks are two common security threats that hang over
deep learning. Both of them harness task-irrelevant features of data in their implementation …

Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

Modern NLP defines the task of style transfer as modifying the style of a given sentence
without appreciably changing its semantics, which implies that the outputs of style transfer …

The prompt-based learning paradigm has gained much research attention recently. It has
achieved state-of-the-art performance on several NLP tasks, especially in the few-shot …

We consider repair tasks: given a critic (eg, compiler) that assesses the quality of an input,
the goal is to train a fixer that converts a bad example (eg, code with syntax errors) into a …

Abstract Machine Translation (MT) has made significant progress in recent years using deep
learning, especially after the emergence of large language models (LLMs) such as GPT-3 …