As the applications of large language models (LLMs) expand across diverse fields, their
ability to adapt to ongoing changes in data, tasks, and user preferences becomes crucial …

Continual learning (CL) is a learning paradigm that emulates the human capability of
learning and accumulating knowledge continually without forgetting the previously learned …

Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis
(ABSA), aiming to analyze and understand people's opinions at the aspect level, has been …

This paper proposed a new online continual learning approach called OCM based on
mutual information (MI) maximization. It achieves two objectives that are critical in dealing …

Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving
two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge …

Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing and computer vision. Unlike traditional neural …

Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic
forgetting (CF) problem. Existing research has achieved remarkable results in overcoming …

RNA modifications are important for deciphering the function of cells and their regulatory
mechanisms. In recent years, researchers have developed many deep learning methods to …

Lifelong event detection aims to incrementally update a model with new event types and
data while retaining the capability on previously learned old types. One critical challenge is …