Le Mens, GaëlGallego, Aina2025-05-202025-05-202025Le Mens G, Gallego A. Positioning political texts with large language models by asking and averaging. Polit Anal. 2025 Jan 27. DOI: 10.1017/pan.2024.291047-1987http://hdl.handle.net/10230/70440Data de publicació electrònica: 27-01-2025We 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.application/pdfeng© The Author(s), 2025. Published by Cambridge University Press on behalf of The Society for Political Methodology. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.info:eu-repo/semantics/article2025-05-20http://dx.doi.org/10.1017/pan.2024.29LLMIdeologyScalingText as datainfo:eu-repo/semantics/openAccess