Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy
and rectifying errors remains elusive. To this end, the past few years have witnessed a surge …

The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Recent large-scale vision-language models (VLMs) have demonstrated remarkable
capabilities in understanding and generating textual descriptions for visual content …

Model editing aims to precisely alter the behaviors of large language models (LLMs) in
relation to specific knowledge, while leaving unrelated knowledge intact. This approach has …

Large language models (LLMs) have shown great success in various Natural Language
Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep …

Abstract Knowledge editing injects knowledge updates into language models to keep them
correct and up-to-date. However, its current evaluations deviate significantly from practice …

Large language models (LLMs) exhibit superior performance on various natural language
tasks, but they are susceptible to issues stemming from outdated data and domain-specific …

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …