@inproceedings{xu-etal-2025-memorizing,
title = "Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning",
author = "Xu, Ruoxi and
Ji, Yunjie and
Cao, Boxi and
Lu, Yaojie and
Lin, Hongyu and
Han, Xianpei and
He, Ben and
Sun, Yingfei and
Li, Xiangang and
Sun, Le",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1392/",
doi = "10.18653/v1/2025.acl-long.1392",
pages = "28682--28693",
ISBN = "979-8-89176-251-0",
abstract = "Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection. However, current research on knowledge injection remains superficial, mainly focusing on knowledge memorization and retrieval. This paper proposes a four-tier knowledge injection framework that systematically defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. Based on this framework, we introduce DeepKnowledge, a synthetic experimental testbed designed for fine-grained evaluation of the depth of knowledge injection across three knowledge types (novel, incremental, and updated). We then explore various knowledge injection scenarios and evaluate the depth of knowledge injection for each scenario on the benchmark. Experimental results reveal key factors to reach each level of knowledge injection for LLMs and establish a mapping between the levels of knowledge injection and the corresponding suitable injection methods, aiming to provide a comprehensive approach for efficient knowledge injection across various levels. The code is available at [https://github.com/icip-cas/Knowledge-Learning-Toolkits](https://github.com/icip-cas/Knowledge-Learning-Toolkits)."
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<abstract>Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection. However, current research on knowledge injection remains superficial, mainly focusing on knowledge memorization and retrieval. This paper proposes a four-tier knowledge injection framework that systematically defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. Based on this framework, we introduce DeepKnowledge, a synthetic experimental testbed designed for fine-grained evaluation of the depth of knowledge injection across three knowledge types (novel, incremental, and updated). We then explore various knowledge injection scenarios and evaluate the depth of knowledge injection for each scenario on the benchmark. Experimental results reveal key factors to reach each level of knowledge injection for LLMs and establish a mapping between the levels of knowledge injection and the corresponding suitable injection methods, aiming to provide a comprehensive approach for efficient knowledge injection across various levels. The code is available at [https://github.com/icip-cas/Knowledge-Learning-Toolkits](https://github.com/icip-cas/Knowledge-Learning-Toolkits).</abstract>
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%0 Conference Proceedings
%T Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning
%A Xu, Ruoxi
%A Ji, Yunjie
%A Cao, Boxi
%A Lu, Yaojie
%A Lin, Hongyu
%A Han, Xianpei
%A He, Ben
%A Sun, Yingfei
%A Li, Xiangang
%A Sun, Le
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F xu-etal-2025-memorizing
%X Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection. However, current research on knowledge injection remains superficial, mainly focusing on knowledge memorization and retrieval. This paper proposes a four-tier knowledge injection framework that systematically defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. Based on this framework, we introduce DeepKnowledge, a synthetic experimental testbed designed for fine-grained evaluation of the depth of knowledge injection across three knowledge types (novel, incremental, and updated). We then explore various knowledge injection scenarios and evaluate the depth of knowledge injection for each scenario on the benchmark. Experimental results reveal key factors to reach each level of knowledge injection for LLMs and establish a mapping between the levels of knowledge injection and the corresponding suitable injection methods, aiming to provide a comprehensive approach for efficient knowledge injection across various levels. The code is available at [https://github.com/icip-cas/Knowledge-Learning-Toolkits](https://github.com/icip-cas/Knowledge-Learning-Toolkits).
%R 10.18653/v1/2025.acl-long.1392
%U https://aclanthology.org/2025.acl-long.1392/
%U https://doi.org/10.18653/v1/2025.acl-long.1392
%P 28682-28693
Markdown (Informal)
[Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning](https://aclanthology.org/2025.acl-long.1392/) (Xu et al., ACL 2025)
ACL
- Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, and Le Sun. 2025. Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28682–28693, Vienna, Austria. Association for Computational Linguistics.