Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning

2025-04-01

AI Summary

Large language models (LLMs) struggle to adapt to dynamic knowledge evolution due to their static parameterization; existing knowledge injection methods predominantly focus on memory and retrieval, lacking systematic modeling and evaluation of higher-order capabilities—namely reasoning and association. Method: We propose a four-layer knowledge injection framework—Memory → Retrieval → Reasoning → Association—formally defining hierarchical progression and establishing its mapping to injection depth. We further introduce DeepKnowledge, a synthetic benchmark enabling fine-grained, depth-aware evaluation across three knowledge evolution types: novel, incremental, and updated. Contribution/Results: Through layered modeling and multi-scenario experiments, we demonstrate that advancing to reasoning and association layers significantly enhances LLMs’ dynamic knowledge adaptation. Our framework provides principled guidance for method selection across diverse knowledge evolution scenarios, bridging a critical gap between knowledge injection and high-level cognitive capabilities.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection.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.

Problem

• Addressing outdated information in static large language models
• Developing a framework for deep knowledge injection levels
• Evaluating knowledge injection depth across diverse scenarios

Innovation

• Four-tier framework for systematic knowledge injection
• DeepKnowledge testbed for fine-grained evaluation
• Mapping injection levels to suitable methods