How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
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Abstract (arXiv)
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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
Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 153 of its 196 references that are in the map. First posted on arXiv 11 Oct 2023.
Builds on
153 of 196 references.
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50 most cited of 153 All 153 (JSON)
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Citing papers per year: 2023: 2; 2024: 13; 2025: 15; 2026: 3.
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- 2025Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation10 Apr 2025
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