Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Recent advances in updating large language models without retraining are reviewed, highlighting challenges and future directions.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 5
- Hosting
- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2310.07343ARXIV-DEFAULT
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- Semantic Scholar