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Large Language Models in Economics

In: The Palgrave Handbook of Economics and Language

Author

Listed:
  • Elliott Ash

    (ETH Zurich)

  • Stephen Hansen

    (UCL, IFS, and CEPR)

  • Yabra Muvdi

    (Digitec Galaxus AG)

  • Claudia Marangon

    (ETH Zurich)

Abstract

This chapter explores the transformative impact of large language models (LLMs) on text analysis in economics. We trace the evolution from traditional methods like bag-of-words to advanced models such as BERT and GPT, highlighting how these models address limitations in understanding context and allowing higher-order reasoning. Although LLMs are complex, costly, and lacking in transparency, they are powerful tools for research, such as measuring sentiment or predicting metadata associated with documents.

Suggested Citation

  • Elliott Ash & Stephen Hansen & Yabra Muvdi & Claudia Marangon, 2026. "Large Language Models in Economics," Springer Books, in: Shlomo Weber & Victor Ginsburgh (ed.), The Palgrave Handbook of Economics and Language, edition 0, chapter 0, pages 191-210, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-88240-1_8
    DOI: 10.1007/978-3-031-88240-1_8
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    Cited by:

    1. Vegard H. Larsen & Leif Anders Thorsrud, 2026. "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," Working Papers No 02/2026, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

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