用智能代理提升大模型研究国会制度的效率
Agent-Enhanced Large Language Models for Researching Political Institutions
- 给大模型接入工具函数，使其能自动查资料、处理数据
- 实测可降低复制和扩展政治学研究的成本
- 适合想高效分析国会数据的研究者使用
大语言模型在政治科学中的应用日益广泛。本文展示，当大模型被赋予预设功能与专用工具后，可作为动态智能体，高效完成数据收集、预处理与分析等任务。核心是代理增强型检索生成（Agentic RAG），使模型具备调用外部知识库的能力。除信息检索外，还可集成文档摘要、转录编码、定性变量分类及统计建模等模块化工具。为验证该方法潜力，我们提出了CongressRA——一个专为研究美国国会而设计的LLM智能体。通过实例表明，此类智能体能显著降低基于领域数据开展实证研究的复制、验证与拓展成本。
原文摘要 · Abstract (English)
The applications of Large Language Models (LLMs) in political science are rapidly expanding. This paper demonstrates how LLMs, when augmented with predefined functions and specialized tools, can serve as dynamic agents capable of streamlining tasks such as data collection, preprocessing, and analysis. Central to this approach is agentic retrieval-augmented generation (Agentic RAG), which equips LLMs with action-calling capabilities for interaction with external knowledge bases. Beyond information retrieval, LLM agents may incorporate modular tools for tasks like document summarization, transcript coding, qualitative variable classification, and statistical modeling. To demonstrate the potential of this approach, we introduce CongressRA, an LLM agent designed to support scholars studying the U.S. Congress. Through this example, we highlight how LLM agents can reduce the costs of replicating, testing, and extending empirical research using the domain-specific data that drives the study of political institutions.
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