结构与解构共塑通用知识引擎，提升模型透明性与适应性。
Structure and Destructure: Dual Forces in the Making of Knowledge Engines
- 提出结构与解构双力协同机制，融合符号系统与大规模数据
- 周期性嵌入重置显著增强模型对未知场景的泛化能力
- 为可解释、可控制的智能系统提供新构建范式
自然语言处理中知识引擎的构建长期受两种看似迥异范式的影响：一种基于结构化符号系统（如知识图谱），另一种则依赖海量无结构数据驱动的大规模模型。本文揭示二者深层关联，提出结构与解构双力并行机制：结构用于组织已知符号交互，解构通过周期性嵌入重置提升模型可塑性与对未知情境的泛化能力。这一统一框架为构建具备透明性、可控性与自适应性的通用知识引擎提供了新范式。
原文摘要 · Abstract (English)
The making of knowledge engines in natural language processing has been shaped by two seemingly distinct paradigms: one grounded in structure, the other driven by massively available unstructured data. The structured paradigm leverages predefined symbolic interactions, such as knowledge graphs, as priors and designs models to capture them. In contrast, the unstructured paradigm centers on scaling transformer architectures with increasingly vast data and model sizes, as seen in modern large language models. Despite their divergence, this thesis seeks to establish conceptual connections bridging these paradigms. Two complementary forces, structure and destructure, emerge across both paradigms: structure organizes seen symbolic interactions, while destructure, through periodic embedding resets, improves model plasticity and generalization to unseen scenarios. These connections form a new recipe for developing general knowledge engines that can support transparent, controllable, and adaptable intelligent systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。