Large language models (LLMs) like Claude, Mistral IA, and GPT-4 excel in NLP but lack structured knowledge, leading to factual inconsistencies. We address this by integrating Knowledge Graphs (KGs) via KG-BERT to enhance grounding and reasoning. Experiments show significant gains in knowledge-intensive tasks such as question answering and entity linking. This approach improves factual reliability and enables more context-aware next-generation LLMs.
翻译:以Claude、Mistral IA和GPT-4为代表的大型语言模型在自然语言处理任务中表现卓越,但缺乏结构化知识,导致事实一致性不足。本研究通过KG-BERT集成知识图谱,以增强模型的语义基础和推理能力。实验表明,该方法在问答和实体链接等知识密集型任务中取得显著性能提升。该策略不仅提高了事实可靠性,还为构建更具上下文感知能力的下一代大型语言模型提供了新途径。