LegiGPT: Party Politics and Transport Policy with Large Language Model

• URL: http://arxiv.org/abs/2506.16692v2
• Date: Sat, 28 Jun 2025 03:42:00 GMT
• Title: LegiGPT: Party Politics and Transport Policy with Large Language Model
• Authors: Hyunsoo Yun, Eun Hak Lee,
• Abstract summary: This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals.<n>Political affiliations and sponsor characteristics were used to identify key factors shaping transportation policymaking.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• NomicLaw: Emergent Trust and Strategic Argumentation in LLMs During Collaborative Law-Making [6.56837855642886]
  We introduce NomicLaw, a structured multi-agent simulation where LLMs engage in collaborative law-making.<n>We quantitatively measure trust and reciprocity via voting patterns and qualitatively assess how agents use strategic language to justify proposals.<n>Our results highlight the latent social reasoning and persuasive capabilities of ten open-source LLMs and provide insights into the design of future AI systems.
  arXiv  Detail & Related papers  (2025-08-07T12:49:44Z)
• Unveiling Political Influence Through Social Media: Network and Causal Dynamics in the 2022 French Presidential Election [0.0]
  During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks.<n>Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate.<n>Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases.
  arXiv  Detail & Related papers  (2025-06-19T16:35:02Z)
• KOKKAI DOC: An LLM-driven framework for scaling parliamentary representatives [0.0]
  This paper introduces an LLM-driven framework designed to accurately scale the political issue stances of parliamentary representatives.<n>By leveraging advanced natural language processing techniques and large language models, the proposed methodology refines and enhances previous approaches.<n>The framework incorporates three major innovations: (1) de-noising parliamentary speeches via summarization to produce cleaner, more consistent opinion embeddings; (2) automatic extraction of axes of political controversy from legislators' speech summaries; and (3) a diachronic analysis that tracks the evolution of party positions over time.
  arXiv  Detail & Related papers  (2025-05-11T21:03:53Z)
• Measuring Interest Group Positions on Legislation: An AI-Driven Analysis of Lobbying Reports [17.4092661362727]
  Special interest groups ( SIGs) in the U.S. participate in a range of political activities to influence policy decisions in the legislative and executive branches.<n>Despite the significance of understanding SIGs' policy positions, empirical challenges in observing them have led researchers to rely on indirect measurements.<n>This study introduces the first large-scale effort to directly measure and predict a wide range of bill positions.
  arXiv  Detail & Related papers  (2025-04-21T17:54:47Z)
• Few-shot Policy (de)composition in Conversational Question Answering [54.259440408606515]
  We propose a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting.<n>We show that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies.<n>We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning.
  arXiv  Detail & Related papers  (2025-01-20T08:40:15Z)
• Political Actor Agent: Simulating Legislative System for Roll Call Votes Prediction with Large Language Models [9.0463587094323]
  Political Actor Agent (PAA) is a novel framework that utilizes Large Language Models to overcome limitations.<n>By employing role-playing architectures and simulating legislative system, PAA provides a scalable and interpretable paradigm for predicting roll-call votes.<n>We conducted comprehensive experiments using voting records from the 117-118th U.S. House of Representatives, validating the superior performance and interpretability of PAA.
  arXiv  Detail & Related papers  (2024-12-10T03:06:28Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Leveraging Knowledge Graphs and LLMs to Support and Monitor Legislative Systems [0.0]
  This work investigates how Legislative Knowledge Graphs and LLMs can synergize and support legislative processes. To this aim, we develop Legis AI Platform, an interactive platform focused on Italian legislation that enhances the possibility of conducting legislative analysis.
  arXiv  Detail & Related papers  (2024-09-20T06:21:03Z)
• Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis [48.14390493099495]
  How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance.<n>This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos.
  arXiv  Detail & Related papers  (2024-09-15T03:17:38Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Modelling Political Coalition Negotiations Using LLM-based Agents [53.934372246390495]
  We introduce coalition negotiations as a novel NLP task, and model it as a negotiation between large language model-based agents. We introduce a multilingual dataset, POLCA, comprising manifestos of European political parties and coalition agreements over a number of elections in these countries. We propose a hierarchical Markov decision process designed to simulate the process of coalition negotiation between political parties and predict the outcomes.
  arXiv  Detail & Related papers  (2024-02-18T21:28:06Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings [0.0]
  The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment. The paper additionally introduces the first domain-specific multilingual transformer language model for political science applications.
  arXiv  Detail & Related papers  (2023-09-18T14:01:06Z)
• Entity Graph Extraction from Legal Acts -- a Prototype for a Use Case in Policy Design Analysis [52.77024349608834]
  This paper presents a prototype developed to serve the quantitative study of public policy design. Our system aims to automate the process of gathering legal documents, annotating them with Institutional Grammar, and using hypergraphs to analyse inter-relations between crucial entities.
  arXiv  Detail & Related papers  (2022-09-02T10:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.