大型語言模型與知識圖譜在問答系統中的融合：綜述與機遇

摘要

大型語言模型（LLMs）在問答（QA）任務中展現了卓越的性能，這得益於其在自然語言理解與生成方面的優越能力。然而，基於LLM的QA在處理複雜問答任務時仍面臨挑戰，主要由於其推理能力不足、知識更新不及時以及產生幻覺等問題。近期多項研究嘗試將LLMs與知識圖譜（KGs）結合用於QA，以應對上述挑戰。在本篇綜述中，我們提出了一種新的結構化分類法，根據QA的類別以及KG在與LLMs整合時所扮演的角色，對LLMs與KGs結合用於QA的方法進行分類。我們系統性地綜述了LLMs與KGs結合用於QA的最新進展，並從優勢、限制及KG需求等方面對這些方法進行了比較與分析。隨後，我們將這些方法與QA任務對齊，探討它們如何解決不同複雜QA的主要挑戰。最後，我們總結了相關進展、評估指標及基準數據集，並指出了開放性挑戰與未來機遇。

English

Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG's role when integrating with LLMs. We systematically survey state-of-the-art advances in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.

大型語言模型與知識圖譜在問答系統中的融合：綜述與機遇

Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities

摘要

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