用大模型分析韩国会立法，发现党派和议员特征影响交通政策走向
LegiGPT: Party Politics and Transport Policy with Large Language Model
- 用大模型+可解释AI识别交通类法案，分步过滤关键词与语境
- 保守派与进步派议员数量、选区规模是决定立法结果的关键因素
- 揭示两党不同参与方式，适合政策研究与治理分析者参考
由于立法者政治意识形态对立法决策具有重要影响，分析其在交通政策制定中的作用至关重要。本研究提出一种融合大型语言模型（LLM）与可解释人工智能（XAI）的新框架，用于分析韩国第21届国会的立法提案。通过关键词、句子及上下文相关性逐步筛选，使用LLM对交通相关法案进行分类，并利用XAI技术探究政党归属与相关属性之间的关系。结果显示，保守派与进步派提案人数量及比例，以及选区规模与选举人口，是塑造立法结果的关键因素。研究发现两党以不同形式参与立法，如发起或支持提案，共同推动跨党派法案。该整合方法为理解立法动态、指导未来政策制定提供了有效工具，对基础设施规划与治理具有广泛意义。
原文摘要 · Abstract (English)
Given the significant influence of lawmakers' political ideologies on legislative decision-making, analyzing their impact on transportation-related policymaking is of critical importance. This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals. Legislative bill data from South Korea's 21st National Assembly were used to identify key factors shaping transportation policymaking. These include political affiliations and sponsor characteristics. The LLM was employed to classify transportation-related bill proposals through a stepwise filtering process based on keywords, sentences, and contextual relevance. XAI techniques were then applied to examine the relationships between political party affiliation and associated attributes. The results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes. These findings suggest that both parties contributed to bipartisan legislation through different forms of engagement, such as initiating or supporting proposals. This integrated approach offers a valuable tool for understanding legislative dynamics and guiding future policy development, with broader implications for infrastructure planning and governance.
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