用人类决策树引导大模型，分析央行文本的鹰派鸽派立场。
Ornithologist: Towards Trustworthy "Reasoning" about Central Bank Communications
- 用人工决策树引导大模型进行文本分类，提升可解释性。
- 量化央行言论鹰派/鸽派程度，预测利率路径和市场预期。
- 弱监督设计，易扩展至新闻等其他文本源。
我开发了Ornithologist，一种弱监督文本分类系统，用于衡量中央银行文本的鹰派与鸽派程度。该系统采用‘分类引导推理’策略，利用人工编写的决策树指导大语言模型，增强系统的透明度与可解释性，使非专家也能理解。同时降低了幻觉风险。相比传统分类系统，所需标注数据更少，可轻松拓展至其他文本来源（如新闻）而无需大幅修改。对澳大利亚储备银行（RBA）沟通内容的鹰派/鸽派测量，能有效反映未来现金利率路径及市场预期信息。
原文摘要 · Abstract (English)
I develop Ornithologist, a weakly-supervised textual classification system and measure the hawkishness and dovishness of central bank text. Ornithologist uses ``taxonomy-guided reasoning'', guiding a large language model with human-authored decision trees. This increases the transparency and explainability of the system and makes it accessible to non-experts. It also reduces hallucination risk. Since it requires less supervision than traditional classification systems, it can more easily be applied to other problems or sources of text (e.g. news) without much modification. Ornithologist measurements of hawkishness and dovishness of RBA communication carry information about the future of the cash rate path and of market expectations.
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