标题： 记忆问题：我们能信任大型语言模型的经济预测吗？ 显示英文标题

标题： The Memorization Problem: Can We Trust LLMs' Economic Forecasts?

摘要： 大型语言模型（LLMs）在它们的训练数据覆盖的时期内不能被信任用于经济预测。 我们提供了对LLMs记忆经济和金融数据（包括主要经济指标、新闻标题、股票回报率和会议电话）的第一个系统性评估。 我们的研究结果显示，LLMs可以完美地回忆起它们知识截止日期前的关键经济变量的确切数值。 这种回忆似乎在不同的日期和数据类型之间随机分布。 这种选择性的完美记忆造成一个基本问题——在测试知识截止日期前的预测能力时，我们无法区分LLMs是在进行预测还是仅仅访问记忆中的数据。 尊重历史数据边界的明确指示未能阻止LLMs在预测任务中达到回忆水平的准确性。 此外，LLMs似乎擅长从最少的上下文线索中重构被掩盖的实体，这表明掩蔽提供的保护不足以防止动机推理。 我们的研究结果引发了关于使用LLMs来预测历史数据或回测交易策略的担忧，因为它们看似成功的预测可能只是反映记忆而不是真正的经济洞见。 任何未来知识会改变LLMs输出的应用都可能受到记忆的影响。 相比之下，与数据污染缺失一致的是，LLMs在其知识截止日期后无法回忆起数据。 显示英文摘要

摘要： Large language models (LLMs) cannot be trusted for economic forecasts during periods covered by their training data. We provide the first systematic evaluation of LLMs' memorization of economic and financial data, including major economic indicators, news headlines, stock returns, and conference calls. Our findings show that LLMs can perfectly recall the exact numerical values of key economic variables from before their knowledge cutoff dates. This recall appears to be randomly distributed across different dates and data types. This selective perfect memory creates a fundamental issue -- when testing forecasting capabilities before their knowledge cutoff dates, we cannot distinguish whether LLMs are forecasting or simply accessing memorized data. Explicit instructions to respect historical data boundaries fail to prevent LLMs from achieving recall-level accuracy in forecasting tasks. Further, LLMs seem exceptional at reconstructing masked entities from minimal contextual clues, suggesting that masking provides inadequate protection against motivated reasoning. Our findings raise concerns about using LLMs to forecast historical data or backtest trading strategies, as their apparent predictive success may merely reflect memorization rather than genuine economic insight. Any application where future knowledge would change LLMs' outputs can be affected by memorization. In contrast, consistent with the absence of data contamination, LLMs cannot recall data after their knowledge cutoff date.