# Positioning Political Texts with Large Language Models by Asking and Averaging

arXiv: https://arxiv.org/abs/2311.16639 · HTML: https://mlsanity.com/paper/2311.16639/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Large Language Models Can Be Used to Estimate the Latent Positions of Politicians](https://mlsanity.com/paper/2303.12057/) (2023)

## Built on by (most cited first, 13 of 13)

- [Large Language Models in Politics and Democracy: A Comprehensive Survey](https://mlsanity.com/paper/2412.04498/) (2024)
- [AI and Human Oversight: A Risk-Based Framework for Alignment](https://mlsanity.com/paper/2510.09090/) (2025)
- [Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches](https://mlsanity.com/paper/2506.06540/) (2025)
- [Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages](https://mlsanity.com/paper/2508.06435/) (2025)
- [Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms](https://mlsanity.com/paper/2511.13238/) (2025)
- [Validating Political Position Predictions of Arguments](https://mlsanity.com/paper/2602.18351/) (2026)
- [Reproducibility is not construct validity: LLM measurement of institutionally situated communication](https://mlsanity.com/paper/2609.19866/) (2026)
- [Beyond the Link: Assessing LLMs' ability to Classify Political Content across Global Media](https://mlsanity.com/paper/2506.17435/) (2025)
- [Elite Polarization in European Parliamentary Speeches: a Novel Measurement Approach Using Large Language Models](https://mlsanity.com/paper/2507.06658/) (2025)
- [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)
- [The geometry of online conversations and the causal antecedents of conflictual discourse](https://mlsanity.com/paper/2602.15600/) (2026)
- [The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel](https://mlsanity.com/paper/2606.23042/) (2026)
- [Validating LLMs in social science: Epistemic threats and emerging norms](https://mlsanity.com/paper/2607.07915/) (2026)

## Abstract (arXiv)

We use instruction-tuned Large Language Models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer texts such as UK party manifestos or EU policy speeches given in 10 different languages. The correlations between the position estimates obtained with the best LLMs and benchmarks based on text coding by experts, crowdworkers, or roll call votes exceed .90. This approach is generally more accurate than the positions obtained with supervised classifiers trained on large amounts of research data. Using instruction-tuned LLMs to position texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.