CB-LMs: language models for central banking

Leonardo Gambacorta, Byeungchun Kwon, Taejin Park, Pietro Patelli and Sonya Zhu

No 1215, BIS Working Papers from Bank for International Settlements

Abstract: We introduce central bank language models (CB-LMs) - specialised encoder-only language models retrained on a comprehensive corpus of central bank speeches, policy documents and research papers. We show that CB-LMs outperform their foundational models in predicting masked words in central bank idioms. Some CB-LMs not only outperform their foundational models, but also surpass state-of-the-art generative Large Language Models (LLMs) in classifying monetary policy stance from Federal Open Market Committee (FOMC) statements. In more complex scenarios, requiring sentiment classification of extensive news related to the US monetary policy, we find that the largest LLMs outperform the domain-adapted encoder-only models. However, deploying such large LLMs presents substantial challenges for central banks in terms of confidentiality, transparency, replicability and cost-efficiency.

Keywords: large language models; gen AI; central banks; monetary policy analysis (search for similar items in EconPapers)
JEL-codes: C55 C63 E58 G17 (search for similar items in EconPapers)
Date: 2024-10
New Economics Papers: this item is included in nep-ain, nep-ban, nep-big, nep-cba and nep-mon
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