Revealing economic facts: LLMs know more than they say

Marcus Buckmann, Quynh Anh Nguyen and Ed Hill
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Marcus Buckmann: Bank of England
Quynh Anh Nguyen: Bank of England
Ed Hill: Bank of England

No 1150, Bank of England Staff Working Paper series from Bank of England

Abstract: We investigate whether hidden states of large language models (LLMs) can be used to estimate and impute economic and financial statistics. Focusing on county-level (eg unemployment) and firm-level (eg total assets) variables, we show that a linear regression trained on the hidden states of open-source LLMs outperforms the models' own text outputs. This indicates that internal representations encode richer economic information than is revealed directly in generated responses. A learning curve analysis shows that, in many cases, only a few dozen labelled examples suffice for training. We further propose a transfer learning method that improves estimation accuracy without requiring any labelled data for the target variable. Finally, we demonstrate the practical utility of hidden states in data imputation and super-resolution tasks.

Keywords: Large language models; embeddings; economic statistics; data imputation. (search for similar items in EconPapers)
JEL-codes: C21 C45 C81 (search for similar items in EconPapers)
Pages: 36
Date: 2025-10-31
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:023272

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