How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
It addresses the problem of keeping LLMs current for users and developers, but it is incremental as it synthesizes existing research rather than introducing new methods.
This paper reviews recent methods for updating large language models (LLMs) with new world knowledge without full retraining, categorizing and comparing approaches to address the issue of models becoming outdated after deployment.
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field. We release the paper list at https://github.com/hyintell/awesome-refreshing-llms