Revealing economic facts: LLMs know more than they say

• URL: http://arxiv.org/abs/2505.08662v1
• Date: Tue, 13 May 2025 15:24:08 GMT
• Title: Revealing economic facts: LLMs know more than they say
• Authors: Marcus Buckmann, Quynh Anh Nguyen, Edward Hill,
• Abstract summary: We investigate whether the hidden states of large language models (LLMs) can be used to estimate and impute economic statistics.<n>We show that a simple linear model trained on the hidden states of open-source LLMs outperforms the models' text outputs.
• Score: 1.433758865948252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate whether the hidden states of large language models (LLMs) can be used to estimate and impute economic and financial statistics. Focusing on county-level (e.g. unemployment) and firm-level (e.g. total assets) variables, we show that a simple linear model trained on the hidden states of open-source LLMs outperforms the models' text outputs. This suggests that hidden states capture richer economic information than the responses of the LLMs reveal directly. A learning curve analysis indicates that only a few dozen labelled examples are sufficient for training. We also 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-state representations in super-resolution and data imputation tasks.

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