This study explores the integration of large language models (LLMs) into classic inflation nowcasting frameworks, particularly in light of high inflation volatility periods such as the COVID-19 pandemic. We propose InflaBERT, a BERT-based LLM fine-tuned to predict inflation-related sentiment in news. We use this model to produce NEWS, an index capturing the monthly sentiment of the news regarding inflation. Incorporating our expectation index into the Cleveland Fed's model, which is only based on macroeconomic autoregressive processes, shows a marginal improvement in nowcast accuracy during the pandemic. This highlights the potential of combining sentiment analysis with traditional economic indicators, suggesting further research to refine these methodologies for better real-time inflation monitoring. The source code is available at https://github.com/paultltc/InflaBERT.

翻译：本研究探讨了将大语言模型（LLMs）整合到经典通胀即时预测框架中的方法，特别是在COVID-19大流行等高通胀波动时期。我们提出了InflaBERT，一种基于BERT架构并经过微调的大语言模型，用于预测新闻中与通胀相关的情感倾向。利用该模型，我们构建了NEWS指数，用以捕捉月度新闻中关于通胀的情感基调。将我们的预期指数纳入仅基于宏观经济自回归过程的克利夫兰联储模型中，结果显示在疫情期间即时预测精度得到边际改善。这凸显了情感分析与传统经济指标相结合的巨大潜力，并建议通过进一步研究来完善这些方法，以实现更优的实时通胀监测。相关源代码可在https://github.com/paultltc/InflaBERT获取。