Large Language Models in Economics

Elliott Ash, Stephen Hansen, Yabra Muvdi () and Claudia Marangon ()
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Yabra Muvdi: Digitec Galaxus AG
Claudia Marangon: ETH Zurich

Chapter Chapter 8 in The Palgrave Handbook of Economics and Language, 2026, pp 191-210 from Springer

Abstract: 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.

Keywords: Large language models; Transformers; Transformer models; Generative AI; Text-as-data; Natural language processing; Unstructured data; Supervised learning; Semantic embeddings; Narrative analysis; Document similarity (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-031-88240-1_8

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