Knowledge editing has been increasingly adopted to correct the false or outdated
knowledge in Large Language Models (LLMs). Meanwhile, one critical but under-explored …

Transformer architectures have become a dominant paradigm for domains like language
modeling but suffer in many inference settings due to their quadratic-time self-attention …

Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual
information in generated content, despite their superior capacities across tasks. Meanwhile …

Text style transfer (TST) aims to modify the style of a text without altering its original
meaning. Large language models (LLMs) demonstrate superior performance across …

Large language models (LLMs) need knowledge updates to meet the ever-growing world
facts and correct the hallucinated responses, facilitating the methods of lifelong model …

Language models (LMs) can express factual knowledge involving numeric properties such
as Karl Popper was born in 1902. However, how this information is encoded in the model's …

Abstract Language models (LMs) can express factual knowledge involving numeric
properties such as Karl Popper was born in 1902. However, how this information is encoded …

Memorisation is a natural part of learning from real-world data: neural models pick up on
atypical input-output combinations and store those training examples in their parameter …

Large Language Models (LLMs) have recently revolutionized the NLP field, while they still
fall short in some specific down-stream tasks. In the work, we focus on utilizing LLMs to …

As large language models (LLMs) are widely deployed, targeted editing of their knowledge
has become a critical challenge. Recently, advancements in model editing techniques, such …