QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models

This article introduces QSTN, a modular framework designed to improve the reliability of questionnaire inference using Large Language Models (LLMs). The focus is on creating a more robust system for analyzing and understanding questionnaire data. The modular design suggests flexibility and potential for adaptation to different types of questionnaires and LLMs.