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

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 …

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

Incremental Learning (IL) has been a long-standing problem in both vision and Natural
Language Processing (NLP) communities. In recent years, as Pre-trained Language Models …

Thanks to the recent success of Pre-trained Language Models (PLMs), it has become a
promising research direction to develop a universal model (UIE) that can solve all typical …

Existing knowledge-grounded open-domain dialogue generation models often face the
hallucination problem, ie the dialogue generative model will persist in an inappropriate …

Lifelong learning aims to accumulate knowledge and alleviate catastrophic forgetting when
learning tasks sequentially. However, existing lifelong language learning methods only …

We have investigated methods utilizing hierarchical structure information representation in
the semantic parsing task and have devised a method that reinforces the semantic …

Catastrophic forgetting poses a significant challenge in continual learning (CL). In the
context of Natural Language Processing, generative-based rehearsal CL methods have …

Recent advancements in data-driven task-oriented dialogue systems (ToDs) struggle with
incremental learning due to computational constraints and time-consuming issues …