How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

Zhang, Z; Fang, M; Chen, L; Namazi-Rad, MR; Wang, J

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

Zhang, Z

Fang, M Chen, L

Namazi-Rad, MR Wang, J

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Publication Type:: Conference Proceeding
Citation:: EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, 2023, pp. 8289-8311
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Z https://orcid.org/0000-0002-0380-3808
dc.contributor.author	Fang, M
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Namazi-Rad, MR
dc.contributor.author	Wang, J
dc.date.accessioned	2024-03-04T05:57:21Z
dc.date.available	2024-03-04T05:57:21Z
dc.date.issued	2023-01-01
dc.identifier.citation	EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, 2023, pp. 8289-8311
dc.identifier.isbn	9798891760608
dc.identifier.uri	http://hdl.handle.net/10453/176081
dc.description.abstract	Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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.
dc.language	en
dc.relation	TPG Telecom Limited
dc.relation.ispartof	EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-04T05:57:14Z
pubs.publication-status	Published

Abstract:

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.

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http://hdl.handle.net/10453/176081