Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912302391885824 |
|---|---|
| author | Xu, Ruoxi Ji, Yunjie Cao, Boxi Lu, Yaojie Lin, Hongyu Han, Xianpei He, Ben Sun, Yingfei Li, Xiangang Sun, Le |
| author_facet | Xu, Ruoxi Ji, Yunjie Cao, Boxi Lu, Yaojie Lin, Hongyu Han, Xianpei He, Ben Sun, Yingfei Li, Xiangang Sun, Le |
| contents | Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning Xu, Ruoxi Ji, Yunjie Cao, Boxi Lu, Yaojie Lin, Hongyu Han, Xianpei He, Ben Sun, Yingfei Li, Xiangang Sun, Le Computation and Language Artificial Intelligence Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection.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. |
| title | Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00472 |