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<p>To promote the scientific use of large language models (LLMs), we suggest that researchers in the political and social sciences refocus on the scientific goal of inference. We suggest that this refocus will improve the accumulation of shared scientific knowledge about these tools and their uses in the social sciences. We discuss the challenges and opportunities related to scientific inference with LLMs, using validation of model output as an illustrative case for discussion. We then propose a set of guidelines related to establishing the failure and success of LLMs when completing particular tasks and discuss how to make inferences from these observations.</p>

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Arti-‘fickle’ intelligence: using LLMs as a tool for inference in the political and social sciences

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Lisa P. Argyle,
  • Ethan C. Busby,
  • Joshua R. Gubler,
  • Bryce Hepner,
  • Alex Lyman,
  • David Wingate

摘要

To promote the scientific use of large language models (LLMs), we suggest that researchers in the political and social sciences refocus on the scientific goal of inference. We suggest that this refocus will improve the accumulation of shared scientific knowledge about these tools and their uses in the social sciences. We discuss the challenges and opportunities related to scientific inference with LLMs, using validation of model output as an illustrative case for discussion. We then propose a set of guidelines related to establishing the failure and success of LLMs when completing particular tasks and discuss how to make inferences from these observations.