Large Language Models in Politics and Democracy: A Comprehensive Survey

• URL: http://arxiv.org/abs/2412.04498v2
• Date: Mon, 16 Dec 2024 05:27:41 GMT
• Title: Large Language Models in Politics and Democracy: A Comprehensive Survey
• Authors: Goshi Aoki,
• Abstract summary: Large language models (LLMs) offer potential across various domains, including policymaking, political communication, analysis, and governance. LLMs offer opportunities to enhance efficiency, inclusivity, and decision-making in political processes. They also present challenges related to bias, transparency, and accountability.
• Score: 0.0
• License:
• Abstract: The advancement of generative AI, particularly large language models (LLMs), has a significant impact on politics and democracy, offering potential across various domains, including policymaking, political communication, analysis, and governance. This paper surveys the recent and potential applications of LLMs in politics, examining both their promises and the associated challenges. This paper examines the ways in which LLMs are being employed in legislative processes, political communication, and political analysis. Moreover, we investigate the potential of LLMs in diplomatic and national security contexts, economic and social modeling, and legal applications. While LLMs offer opportunities to enhance efficiency, inclusivity, and decision-making in political processes, they also present challenges related to bias, transparency, and accountability. The paper underscores the necessity for responsible development, ethical considerations, and governance frameworks to ensure that the integration of LLMs into politics aligns with democratic values and promotes a more just and equitable society.

Related papers

• Benchmarking LLMs for Political Science: A United Nations Perspective [34.000742556609126]
  Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplored. This paper addresses the gap by focusing on the application of LLMs to the United Nations (UN) decision-making process. We introduce a novel dataset comprising publicly available UN Security Council (UNSC) records from 1994 to 2024, including draft resolutions, voting records, and diplomatic speeches.
  arXiv  Detail & Related papers  (2025-02-19T21:51:01Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks. Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Can LLMs advance democratic values? [0.0]
  We argue that LLMs should be kept well clear of formal democratic decision-making processes. They can be put to good use in strengthening the informal public sphere.
  arXiv  Detail & Related papers  (2024-10-10T23:24:06Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• 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)
• Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies [5.958974943807783]
  This study addresses the challenge of understanding political bias in digitized discourse using Large Language Models (LLMs) We present a comprehensive analytical framework, consisting of Partisan Bias Divergence Assessment and Partisan Class Tendency Prediction. Our findings reveal the model's effectiveness in capturing emotional and moral nuances, albeit with some challenges in stance detection.
  arXiv  Detail & Related papers  (2023-11-16T08:57:53Z)

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.