人工智能:使用LLMs作为政治和社会科学中推断的工具
Lisa P Argyle1, Ethan C Busby2, Joshua R Gubler2
1Department of Political Science, Brigham Young University, Provo, UT, US. lpargyle@byu.edu.
Nature computational science
|August 8, 2025
概括
在使用大型语言模型 (LLM) 时,社会科学研究人员应优先考虑科学推断. 这一重点将增强知识积累,并指导研究LLM应用的最佳实践的发展.
科学领域:
- 社会科学 社会科学 社会科学
- 政治科学 政治科学是指政治学.
- 计算社会科学 计算社会科学
背景情况:
- 大型语言模型 (LLM) 提供了新的研究可能性,但需要仔细的科学应用.
- 目前在社会科学中使用的LLM可能无法充分利用其对严格推断的潜力.
研究的目的:
- 在利用LLMs时,倡导重新关注社会科学中的科学推断.
- 为评估LLM绩效和得出科学结论提供一个框架.
主要方法:
- 对LLM科学推理能力的概念分析.
- 讨论基于LLM的社会科学研究中的挑战和机遇.
- 关于验证LLM成果用于研究目的的案例研究.
主要成果:
- 重新关注推理可以改善关于LLMs的共享科学知识的积累.
- 确定LLM成功和失败的明确标准对于可靠的推断至关重要.
- 提出了从LLM观察中得出推断的指导方针.
结论:
- 采用以推理为中心的方法将促进LLM在社会科学中的科学实用性.
- 验证和仔细解释LLM输出是强大的研究的关键.
- 该框架支持在学术研究中负责任地开发和应用LLM.
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