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A Financial Brain Scan of the LLM

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  • Hui Chen
  • Antoine Didisheim
  • Luciano Somoza
  • Hanqing Tian

Abstract

Emerging techniques in computer science make it possible to "brain scan" large language models (LLMs), identify the plain-English concepts that guide their reasoning, and steer them while holding other factors constant. We show that this approach can map LLM-generated economic forecasts to concepts such as sentiment, technical analysis, and timing, and compute their relative importance without reducing performance. We also show that models can be steered to be more or less risk-averse, optimistic, or pessimistic, which allows researchers to correct or simulate biases. The method is transparent, lightweight, and replicable for empirical research in the social sciences.

Suggested Citation

  • Hui Chen & Antoine Didisheim & Luciano Somoza & Hanqing Tian, 2025. "A Financial Brain Scan of the LLM," Papers 2508.21285, arXiv.org.
  • Handle: RePEc:arx:papers:2508.21285
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    References listed on IDEAS

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