提出用户信息需求分类体系，指导大模型、知识图谱与搜索引擎融合研究。
Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions
- 构建用户信息需求的分类框架，涵盖不同复杂度与维度。
- 分析三类技术在满足各类需求时的优劣与互补性。
- 为多模态信息融合研究提供可操作的未来方向指南。
关于大语言模型、知识图谱与搜索引擎如何协同已有广泛讨论，但当前学术界普遍忽视了用户视角。特别是，如何有效应对用户多样化、多层次的信息需求仍存在诸多开放问题。本文提出一个用户信息需求的分类体系，引导我们研究大语言模型、知识图谱与搜索引擎在实际应用中的优缺点及其潜在协同效应。基于此研究，本文进一步提炼出未来研究的发展路线图。
原文摘要 · Abstract (English)
Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is the user perspective. In particular, there remain many open questions regarding how best to address the diverse information needs of users, incorporating varying facets and levels of difficulty. This paper introduces a taxonomy of user information needs, which guides us to study the pros, cons and possible synergies of Large Language Models, Knowledge Graphs and Search Engines. From this study, we derive a roadmap for future research.
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