Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to generate open-ended text instead.Previous research has used a diverse range of methods for generating closed-ended survey responses with LLMs, and a standard practice remains to be identified.In this paper, we systematically investigate the impact that various Survey Response Generation Methods have on predicted survey responses.We present the results of 32 mio.simulated survey responses across 8 Survey Response Generation Methods, 4 political attitude surveys, and 10 openweight language models.We find significant differences between the Survey Response Generation Methods in both individual-level and subpopulation-level alignment.Our results show that Restricted Generation Methods perform best overall, and that reasoning output does not consistently improve alignment.Our work underlines the significant impact that Survey Response Generation Methods have on simulated survey responses, and we develop practical recommendations on the application of Survey Response Generation Methods.

This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users and extract longitudinal aggregates of emotions and attitudes with established questionnaires. We focus our analysis on the beginning of the COVID-19 pandemic that had a strong impact on public opinion and collective emotions. We validate our estimates against representative British survey data and find strong positive and significant correlations for several collective emotions. The estimates obtained are robust across multiple training seeds and prompt formulations, and in line with collective emotions extracted using a traditional classification model trained on labeled data. We demonstrate the flexibility of our method on questions of public opinion for which no pre-trained classifier is available. Our work extends the analysis of affect in LLMs to a longitudinal setting through Temporal Adapters. It enables flexible and new approaches to the longitudinal analysis of social media data.