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Paper short abstract

The study examines how large language models (LLMs) simulate the impact of intergenerational trauma of political socialisation in the German context with a particular focus on whether such simulations are prone to memory-related bias.

Paper long abstract

The rise of large language models (LLMs) presents new methodological opportunities for social science research, particularly in simulating and predicting human behavior. Traditional social science methods often face challenges when applied to studying highly sensitive issues related to intergenerational trauma and memory, due to social desirability bias and difficulties with respondent recruitment. LLMs offer a possibility to address these constraints by simulating attitudes towards the past and the present societal issues for respondents with different demographic purposes. However, before such a possibility can be realistically realized, it must undergo thorough scrutiny, especially given the frequent lack of transparency in LLMs and the risk of bias. To contribute to such scrutiny, we investigate how LLMs perceive the impact of intergenerational trauma on the political attitudes of German citizens. Specifically, we simulate survey responses via a selection of LLMs regarding voting behavior and the perceived relevance of two memory- and identity-formative events: the Holocaust and German reunification. By systematically manipulating participant features, including demographic characteristics and modes of memory/trauma transmission, we explore the reasoning behind LLMs' perceptions of the effects of historical knowledge on human behavior and potential biases affecting such perceptions.