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From implicit parameters to explicit knowledge graphs: A structured knowledge recall framework for bidirectional generalization in LLMs
DOI:10.1016/j.eswa.2026.133844.png)
摘要
En 中文
• 引入基于知识图谱的评估,揭示大语言模型(LLMs)对知识理解的不足。
• 缓解“反转诅咒”现象,增强大语言模型(LLMs)的双向泛化能力。
• 揭示自我回忆的知识在推理任务中优于外部注入的知识。
Keyword:
Large language models
Knowledge graph
Fine-tuning
Knowledge management
Bidirectional generalization
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
CaReKGC: A Causal-Guided Structural Reasoning Framework for LLM-based Knowledge Graph CompletionCaReKGC:一种基于LLM的因果引导结构化推理框架,用于知识图谱补全
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics通过训练动态对“反转诅咒”进行理论理解
Prompting disentangled embeddings for knowledge graph completion with pre-trained language model利用预训练语言模型引导解耦嵌入进行知识图谱补全
Huang, W.; Zhou, G.; Lapata, M.; Vougiouklis, P.; Montella, S.; Pan, J.Z. Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-Tail Facts. Knowl.-Based Syst. 2024, 324, 113648. [Google Scholar] [CrossRef]黄, W.; 周, G.; Lapata, M.; Vougiouklis, P.; Montella, S.; 潘, J.Z. 使用知识图谱为大型语言模型提示,以解决涉及长尾事实的问答问题. 知识基系统. 2024, 324, 113648. [谷歌学术] [ CrossRef ]
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