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 …

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

Despite significant advancements in large language models (LLMs), the rapid and frequent
integration of small-scale experiences, such as interactions with surrounding objects …

Building a human-like system that continuously interacts with complex environments--
whether simulated digital worlds or human society--presents several key challenges. Central …

Universal information extraction (Universal IE) aims to develop one model capable of
solving multiple IE target tasks. Previous works have enhanced extraction performance of …

Recently, large language models (LLMs) have demonstrated remarkable capabilities in a
wide range of tasks. Typically, an LLM is pre-trained on large corpora and subsequently fine …

The extraordinary results achieved by Large Language Models are paired with issues that
are critical in real-world applications. The costs of inference and, in particular, training are …

Recent years have witnessed a spurt in large language models for its strong generalization
performance, many continual instruction tuning methods based on parameter-efficient tuning …

Large Language Models have shown the capability to perform well on various tasks. To
achieve this, the models are trained on vast amounts of data. However, this knowledge can …