Economists are increasingly applying natural language processing (NLP) techniques to analyze monetary policy communications. While these studies offer valuable insights, they often rely on language models trained on collections of general texts. This limitation may hinder the models’ ability to fully capture the nuances unique to central banking and monetary economics. Recent literature suggests that retraining language models on domain-specific data can enhance performance in specialized NLP analyses.
Researchers from the Bank for International Settlements introduce CB-LMs (central bank language models) – language models retrained on a large-scale collection of central banking texts. By using prominent models like BERT and RoBERTa and adding texts tailored to central banking – including speeches, policy notes and research papers – CB-LMs capture domain-specific semantics, terminologies and contextual nuances. Our primary goal is to develop and publicly release CB-LMs to advance NLP analysis in monetary economics and central banking. Additionally, their comprehensive assessment of different large language models (LLMs) across various training settings provides insights into model selection tailored to central bankers’ specific tasks and technical requirements.
Researchers found that CB-LMs outperform their foundational models in predicting masked words within central bank idioms. Some CB-LMs surpass not only their original models but also state-of-the-art generative LLMs in classifying monetary policy stances from Federal Open Market Committee statements. CB-LMs excel at understanding nuanced expressions of monetary policy, which could make them valuable tools for central banks in real-time analysis and decision-making. Nonetheless, in more challenging scenarios – such as with limited data for fine-tuning and processing longer text inputs – the largest LLMs, like ChatGPT-4 and Llama-3 70B, may outperform CB-LMs. Deploying these LLMs, however, presents substantial challenges for central banks regarding confidentiality, transparency, replicability and cost-efficiency.