May 14, 2024, 4:50 a.m. | Ga\"el Le Mens, Aina Gallego

cs.CL updates on arXiv.org arxiv.org

arXiv:2311.16639v2 Announce Type: replace
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; …

abstract arxiv author chatbot cs.cl document gpt gpt-4 instruction-tuned language language models large language large language models llama llama 3 llms mixtral policy political replace scaling spaces text type

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