标题： 基于信心解读美联储语言：一种由货币政策传导路径引导的基于大语言模型的不确定性感知框架 显示英文标题

标题： Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths

摘要： “Fedspeak”，即美国联邦储备系统使用的风格化且常带有微妙含义的语言，包含了隐含的政策信号和战略立场。联邦公开市场委员会战略性地使用Fedspeak作为一种沟通工具，以塑造市场预期并影响国内和全球经济状况。因此，自动解析和解释Fedspeak是一个高影响力挑战，对金融预测、算法交易和数据驱动的政策分析具有重要意义。在本文中，我们提出了一种基于大语言模型、具备不确定性感知的框架，用于解读Fedspeak并分类其背后的货币政策立场。技术上，为了丰富Fedspeak文本的语义和上下文表示，我们结合了基于货币政策传导机制的领域特定推理。我们进一步引入了一个动态不确定性解码模块，以评估模型预测的置信度，从而提高分类准确性和模型可靠性。实验结果表明，我们的框架在政策立场分析任务中达到了最先进的性能。此外，统计分析显示感知不确定性与模型错误率之间存在显著正相关，验证了感知不确定性作为诊断信号的有效性。 显示英文摘要

摘要： "Fedspeak", the stylized and often nuanced language used by the U.S. Federal Reserve, encodes implicit policy signals and strategic stances. The Federal Open Market Committee strategically employs Fedspeak as a communication tool to shape market expectations and influence both domestic and global economic conditions. As such, automatically parsing and interpreting Fedspeak presents a high-impact challenge, with significant implications for financial forecasting, algorithmic trading, and data-driven policy analysis. In this paper, we propose an LLM-based, uncertainty-aware framework for deciphering Fedspeak and classifying its underlying monetary policy stance. Technically, to enrich the semantic and contextual representation of Fedspeak texts, we incorporate domain-specific reasoning grounded in the monetary policy transmission mechanism. We further introduce a dynamic uncertainty decoding module to assess the confidence of model predictions, thereby enhancing both classification accuracy and model reliability. Experimental results demonstrate that our framework achieves state-of-the-art performance on the policy stance analysis task. Moreover, statistical analysis reveals a significant positive correlation between perceptual uncertainty and model error rates, validating the effectiveness of perceptual uncertainty as a diagnostic signal.