# Political-LLM: Large Language Models in Political Science

arXiv: https://arxiv.org/abs/2412.06864 · HTML: https://mlsanity.com/paper/2412.06864/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2024.

## Built on by (most cited first, 27 of 27)

- [Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey](https://mlsanity.com/paper/2503.15850/) (2025)
- [AD-LLM: Benchmarking Large Language Models for Anomaly Detection](https://mlsanity.com/paper/2412.11142/) (2024)
- [Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameters](https://mlsanity.com/paper/2505.04393/) (2025)
- [Learning from Diverse Reasoning Paths with Routing and Collaboration](https://mlsanity.com/paper/2508.16861/) (2025)
- [A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science](https://mlsanity.com/paper/2412.15291/) (2024)
- [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025)
- [A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models](https://mlsanity.com/paper/2507.05288/) (2025)
- [A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond](https://mlsanity.com/paper/2508.11957/) (2025)
- [Can AI Truly Represent Your Voice in Deliberations? A Comprehensive Study of Large-Scale Opinion Aggregation with LLMs](https://mlsanity.com/paper/2510.05154/) (2025)
- [Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models](https://mlsanity.com/paper/2503.16679/) (2025)
- [PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives](https://mlsanity.com/paper/2505.19558/) (2025)
- [A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities](https://mlsanity.com/paper/2507.08425/) (2025)
- [From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?](https://mlsanity.com/paper/2509.17280/) (2025)
- [Simulating Misinformation Vulnerabilities With Agent Personas](https://mlsanity.com/paper/2511.04697/) (2025)
- [AlignSurvey: A Comprehensive Benchmark for Human Preferences Alignment in Social Surveys](https://mlsanity.com/paper/2511.07871/) (2025)
- [MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models](https://mlsanity.com/paper/2603.00048/) (2026)
- [Advancing Harmful Content Detection in Organizational Research: Integrating Large Language Models with Elo Rating System](https://mlsanity.com/paper/2506.16575/) (2025)
- [Ideology-Based LLMs for Content Moderation](https://mlsanity.com/paper/2510.25805/) (2025)
- [ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech](https://mlsanity.com/paper/2511.08247/) (2025)
- [An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia wars](https://mlsanity.com/paper/2601.06132/) (2026)
- [LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade](https://mlsanity.com/paper/2509.07274/) (2025)
- [ParlAI Vote: A Web Platform for Analyzing Gender and Political Bias in Large Language Models](https://mlsanity.com/paper/2509.16264/) (2025)
- [Reasoning Boosts Opinion Alignment in LLMs](https://mlsanity.com/paper/2603.01214/) (2026)
- [Analysing LLM Persona Generation and Fairness Interpretation in Polarised Geopolitical Contexts](https://mlsanity.com/paper/2603.22837/) (2026)
- [When Predictions Become Regressors: A Split-Sample Correction for Biases in Downstream Inference](https://mlsanity.com/paper/2608.02909/) (2026)
- [Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts](https://mlsanity.com/paper/2608.06123/) (2026)
- [AI Should Facilitate Democratic Deliberation at Scale](https://mlsanity.com/paper/2609.20059/) (2026)

## Abstract (arXiv)

In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In this work, we--a multidisciplinary team of researchers spanning computer science and political science--present the first principled framework termed Political-LLM to advance the comprehensive understanding of integrating LLMs into computational political science. Specifically, we first introduce a fundamental taxonomy classifying the existing explorations into two perspectives: political science and computational methodologies. In particular, from the political science perspective, we highlight the role of LLMs in automating predictive and generative tasks, simulating behavior dynamics, and improving causal inference through tools like counterfactual generation; from a computational perspective, we introduce advancements in data preparation, fine-tuning, and evaluation methods for LLMs that are tailored to political contexts. We identify key challenges and future directions, emphasizing the development of domain-specific datasets, addressing issues of bias and fairness, incorporating human expertise, and redefining evaluation criteria to align with the unique requirements of computational political science. Political-LLM seeks to serve as a guidebook for researchers to foster an informed, ethical, and impactful use of Artificial Intelligence in political science. Our online resource is available at: http://political-llm.org/.