Scaling Political Texts with Large Language Models: Asking a Chatbot Might Be All You Need

• URL: http://arxiv.org/abs/2311.16639v2
• Date: Mon, 13 May 2024 14:16:05 GMT
• Title: Scaling Political Texts with Large Language Models: Asking a Chatbot Might Be All You Need
• Authors: Gaël Le Mens, Aina Gallego,
• Abstract summary: We use instruction-tuned Large Language Models (LLMs) to position political texts within policy and ideological spaces. We illustrate and validate the approach by scaling British party manifestos on the economic, social, and immigration policy dimensions. The correlation between the position estimates obtained with the best LLMs and benchmarks based on coding by experts, crowdworkers or roll call votes exceeds.90.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We use instruction-tuned Large Language Models (LLMs) such as GPT-4, MiXtral, and Llama 3 to position political texts within policy and ideological spaces. We directly ask the LLMs where a text document or its author stand on the focal policy dimension. We illustrate and validate the approach by scaling British party manifestos on the economic, social, and immigration policy dimensions; speeches from a European Parliament debate in 10 languages on the anti- to pro-subsidy dimension; Senators of the 117th US Congress based on their tweets on the left-right ideological spectrum; and tweets published by US Representatives and Senators after the training cutoff date of GPT-4. The correlation between the position estimates obtained with the best LLMs and benchmarks based on coding by experts, crowdworkers or roll call votes exceeds .90. This training-free approach also outperforms supervised classifiers trained on large amounts of data. Using instruction-tuned LLMs to scale 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.

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