美国劳工统计局推迟了一份对未来通胀数据至关重要的年度报告，此举正值外界对美国关键经济数据准确性与政治化风险的担忧日益加剧之际。 周五，美国劳工统计局突然宣布，原定于下周二发布的2024年度消费者支出数据将被"重新安排至稍后日期"。美国劳工统计局并未解释推迟的原 因，也未提供新的发布时间表，仅表示"有更多信息时将通知用户"。 （美国劳工统计局官网显示年度消费者支出数据发布时间将被推迟） 每年9月左右，美国劳工统计局会发布上一年度的年度平均支出数据，例如2023年数据于2024年9月发布，显示所有消费者单位平均支出为77,280 美元（较2022年增长5.9%），税前收入增长8.3%。 这份年度报告是美国政府唯一提供消费者支出和收入完整信息的家庭调查数据，涵盖消费者活动、支出、收入和人口统计信息。更重要的是，该 数据用于确定来年消费者价格指数中特定商品和服务的权重。 缺乏解释的罕见延迟 此次发布的推迟之所以引起市场关注，关键在于其"异乎寻常"的沉默。 美国劳工统计局在公告中仅提及延期，但未提供任何背景说明，美国劳工部（统计局的上级部门）也未对此事发表评论。 分析人士指出，这与去年的情况形成鲜明对比。 本月早些时候 ...

Core Viewpoint - The accuracy of data is crucial for financial services companies utilizing Generative AI (GenAI) and Large Language Models (LLM), as inaccurate or low-quality data can adversely affect company strategy, operations, risk management, and compliance [1][3]. Group 1: Causes of Data Inaccuracy - Data inaccuracy in the financial services sector often arises from multiple factors, including the increasing volume and variety of data sourced from multiple vendors, patents, and third-party sources [4]. - "Hallucination" is a significant challenge in the financial sector regarding Generative AI, where models generate coherent but factually incorrect or misleading information due to their reliance on learned patterns from training data without factual verification [4]. Group 2: Importance of Retrieval-Augmented Generation (RAG) - RAG is a critical technology for improving the accuracy of Generative AI and significantly reducing hallucinations by integrating real data with generated responses [6]. - RAG combines the generative capabilities of LLMs with effective data retrieval systems, allowing for more accurate and contextually relevant answers, especially in financial risk assessments [6]. - RAG enhances the utilization of various data formats, enabling the processing of both structured and unstructured data efficiently, and connects existing legacy systems without the need for costly migrations or retraining of LLMs [7]. Group 3: Benefits of RAG - RAG helps address the main causes of data inaccuracy discussed earlier, providing more accurate answers based on proprietary data and reducing hallucinations [8]. - It allows for the integration of the latest knowledge and user permission management, ensuring that responses are based on up-to-date information [8].