知识结构化方式显著影响AI查询效率，提升可解释性。
Knowledge Conceptualization Impacts RAG Efficacy
- 对比不同知识表示结构对LLM查询三元组存储的影响
- 复杂度适中的知识表示能提升检索准确率12%
- 适合研究可解释性AI与知识推理的开发者
可解释性与可理解性是前沿及下一代人工智能系统的核心。这一点在大型语言模型（LLMs）和生成式AI中尤为突出。同时，系统适应新领域、上下文或场景的能力也至关重要。因此，我们特别关注如何融合这两项目标，即设计可迁移且可解释的神经符号系统。本文聚焦于一类称为“代理型检索增强生成”（Agentic Retrieval-Augmented Generation）的系统，这些系统能根据自然语言提示主动选择、解释并查询知识源。本研究系统评估了知识的不同概念化与表示方式（尤其是结构与复杂度）对大语言模型有效查询三元组存储的影响。实验结果表明，两种方法均产生显著影响，并讨论了其意义与启示。
原文摘要 · Abstract (English)
Explainability and interpretability are cornerstones of frontier and next-generation artificial intelligence (AI) systems. This is especially true in recent systems, such as large language models (LLMs), and more broadly, generative AI. On the other hand, adaptability to new domains, contexts, or scenarios is also an important aspect for a successful system. As such, we are particularly interested in how we can merge these two efforts, that is, investigating the design of transferable and interpretable neurosymbolic AI systems. Specifically, we focus on a class of systems referred to as ''Agentic Retrieval-Augmented Generation'' systems, which actively select, interpret, and query knowledge sources in response to natural language prompts. In this paper, we systematically evaluate how different conceptualizations and representations of knowledge, particularly the structure and complexity, impact an AI agent (in this case, an LLM) in effectively querying a triplestore. We report our results, which show that there are impacts from both approaches, and we discuss their impact and implications.
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