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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.

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Large Language Models in Economics

  • 章节

  • 发表日期：

    2026年

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  • Elliott Ash,
  • Stephen Hansen,
  • Yabra Muvdi,
  • Claudia Marangon

摘要

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.