Large Language Models (LLMs) are increasingly used as proxies for human subjects in social science surveys, but their reliability and susceptibility to known human-like response biases, such as central tendency, opinion floating and primacy bias are poorly understood. This work investigates the response robustness of LLMs in normative survey contexts—we test 18 LLMs on questions taken from the World Values Survey (WVS), applying a comprehensive set of ten perturbations to both question phrasing and answer option structure, resulting in over 334,800 simulated survey interviews. In doing so, we not only reveal LLMs’ vulnerabilities to perturbations but also show that almost all tested models exhibit a consistent recency bias, disproportionately favoring the last-presented answer option. While larger models are generally more robust, all models remain sensitive to semantic variations like paraphrasing and to combined perturbations. This underscores the critical importance of prompt design and robustness testing when using LLMs to generate synthetic survey data.

We introduce QSTN, an open-source Python framework for systematically generating responses from questionnaire-style prompts to support in-silico surveys and annotation tasks with large language models (LLMs). QSTN enables robust evaluation of questionnaire presentation, prompt perturbations, and response generation methods. Our extensive evaluation (>40 million survey responses) shows that question structure and response generation methods have a significant impact on the alignment of generated survey responses with human answers. We also find that answers can be obtained for a fraction of the compute cost, by changing the presentation method. In addition, we offer a no-code user interface that allows researchers to set up robust experiments with LLMs without coding knowledge. We hope that QSTN will support the reproducibility and reliability of LLM-based research in the future.