How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad, Jun Wang
摘要
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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 1 . * Equal contribution 1 We release the paper list at https://github.com/ hyintell/awesome-refreshing-llms and will periodically update it. LLMs align with ever-changing world knowledge Implicit ( §2.1)
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper16
- Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformersYibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon AragamNeurIPS 2024 · 被引用 19 次
- Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A SurveyJiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou 等ACL 2024 · 被引用 15 次
- Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language ModelsTobias Schreieder, Tim Schopf, Michael FärberACL 2026 · 被引用 10 次
- EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving KnowledgeZhiyuan Zhu, Yusheng Liao, Zhe Chen, Yuhao Wang 等ACL 2025 · 被引用 10 次
- KnowledgeSmith: Uncovering Knowledge Updating in LLMs with Model Editing and UnlearningYinyi Luo, Zhexian Zhou, Hao Chen, Kai Qiu 等ICLR 2026 · 被引用 4 次
它引用的顶会 Paper50
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma 等NeurIPS 2022 · 被引用 22,562 次
- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksPatrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni 等NeurIPS 2020 · 被引用 19,162 次
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu 等ICLR 2022 · 被引用 18,833 次
相关 Paper
- Towards Continual Knowledge Learning of Language ModelsJoel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin 等ICLR 2022 · 被引用 204 次
- LOKA: Conflict-Aware LLM Knowledge Update with Adaptive Knowledge MemoryBinchi Zhang, Zhengzhang Chen, Zaiyi Zheng, Jundong Li 等ACL 2026
- Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language ModelsSomanshu Singla, Zhen Wang, Tianyang Liu, Abdullah Ashfaq 等EMNLP 2024 · 被引用 1 次
- Reinforced Lifelong Editing for Language ModelsZherui Li, Houcheng Jiang, Hao Chen, Baolong Bi 等ICML 2025
- HLMEA: Unsupervised Entity Alignment Based on Hybrid Language ModelsXiongnan Jin, Zhilin Wang, Jinpeng Chen, Liu Yang 等AAAI 2025 · 被引用 5 次
