Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …

Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Large language models (LLMs) are not amenable to frequent re-training, due to high
training costs arising from their massive scale. However, updates are necessary to endow …

Benefiting from massive corpora and advanced hardware, large language models (LLMs)
exhibit remarkable capabilities in language understanding and generation. However, their …

The continual learning (CL) ability is vital for deploying large language models (LLMs) in the
dynamic world. Existing methods devise the learning module to acquire task-specific …

Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-
trained language models. Despite being arguably the most parameter-efficient (tuned soft …

Continual learning necessitates the continual adaptation of models to newly emerging tasks
while minimizing the catastrophic forgetting of old ones. This is extremely challenging for …