从“语义检索”到“图谱本体映射”:传统的L2系统依赖粗放的文本切片与向量匹配,这导致AI面对财报或公告时只能做“字面复读”。L3架构则引入了深度的金融知识图谱。在数据进入大模型之前,系统首先对海量的非结构化语料进行“因子化清洗”,提取出具备强逻辑关联的财务指标、政策实体与行情动因。大模型的作用被严格限制为“基于既定事实的自然语言渲染”,而非“事实的推理者”,从而在物理层面彻底切断了幻觉的生成源头。
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Bias risksArtificial intelligence tools are trained on vast quantities of material — articles, images, artwork, research papers, YouTube transcripts, etc. And that means these models often reflect the biases of their creators. While major AI companies try to calibrate their models to avoid offensive or discriminatory statements, these efforts may not always succeed.,这一点在同城约会中也有详细论述
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